<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology-based Data Organization for the Enslaved Project</style></title></titles><dates><year><style  face="normal" font="default" size="100%">In Press</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yinglun Zhang</style></author><author><style face="normal" font="default" size="100%">Sonia Moavenzadeh</style></author><author><style face="normal" font="default" size="100%">Jarrar Amjad</style></author><author><style face="normal" font="default" size="100%">Onur Apul</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Fatih Evrendilek</style></author><author><style face="normal" font="default" size="100%">Torsten Hahmann</style></author><author><style face="normal" font="default" size="100%">Ganga Hettiarachchi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">David Kedrowski</style></author><author><style face="normal" font="default" size="100%">Vasu Kilaru</style></author><author><style face="normal" font="default" size="100%">Prayas Lashkari</style></author><author><style face="normal" font="default" size="100%">Katrina Schweikert</style></author><author><style face="normal" font="default" size="100%">Antony Williams</style></author><author><style face="normal" font="default" size="100%">Hande McGinty</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CompTox Ontology: Leveraging Knowledge Graphs for PFAS Monitoring and Decision-Making</style></title><secondary-title><style face="normal" font="default" size="100%">THE ACM WEB CONFERENCE 2026</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><pub-location><style face="normal" font="default" size="100%">Dubai, United Arab Emirates, April 13-17</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Colby K. Fisher</style></author><author><style face="normal" font="default" size="100%">Thomas Thelen</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Wenwen Li</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Antrea Christou</style></author><author><style face="normal" font="default" size="100%">Ling Cai</style></author><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Anthony D'Onofrio</style></author><author><style face="normal" font="default" size="100%">Andrew Eells</style></author><author><style face="normal" font="default" size="100%">Mitchell Faulk</style></author><author><style face="normal" font="default" size="100%">Zilong Liu</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Bryce D. Mecum</style></author><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Meilin Shi</style></author><author><style face="normal" font="default" size="100%">Yuanyuan Tian</style></author><author><style face="normal" font="default" size="100%">Sizhe Wang</style></author><author><style face="normal" font="default" size="100%">Zhangyu Wang</style></author><author><style face="normal" font="default" size="100%">Joseph Zalewski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment</style></title><secondary-title><style face="normal" font="default" size="100%">Transactions in GIS </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.48550/arXiv.2502.13874</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">30</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Moumita Sen Sarma</style></author><author><style face="normal" font="default" size="100%">Avishek Das</style></author><author><style face="normal" font="default" size="100%">Samatha E. Akkamahadevi</style></author><author><style face="normal" font="default" size="100%">Eugene Y. Vasserman</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neurosymbolic Hidden Neuron Analysis in Convolutional Neural Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Neuro-Symbolic AI: Bridging the Gap Between Neural Networks and Symbolic Reasoning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;This tutorial introduces a step-by-step, deductive pipeline for making the inner workings of neural networks more transparent by assigning human-understandable concepts to hidden neuron activations. The approach automatically maps neuron behavior to symbolic concepts drawn from structured knowledge sources and attaches an error margin to each label, providing a measure of confidence in its precision. While demonstrated in detail on the ADE20k scene dataset---including single-concept neurons, multiple neurons contributing to the same concept, and multi-concept neurons---the method is also applied to the SUN2012 dataset and adapted for a text classification task, highlighting its generalizability across modalities. The chapter is designed to be practical and educational, focusing on a replicable methodology that readers can adapt to varied applications. Through worked examples, visualizations, and evaluation strategies, the tutorial offers a clear, reusable framework for concept-based neuron analysis in both vision and language models.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">8</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Avishek Das</style></author><author><style face="normal" font="default" size="100%">Moumita Sen Sarma</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reasoning in Large Language Models: RAG and Beyond</style></title><secondary-title><style face="normal" font="default" size="100%">Neuro-Symbolic AI: Integrating Neural Networks and Symbolic Reasoning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This chapter presents a comprehensive overview of contemporary approaches that integrate neural networks and large language models (LLMs) with classical symbolic reasoning. We review the evolution of methods designed to embed logical inference within neural architectures and explore recent advances in prompting strategies, hybrid reasoning frameworks, retrieval-augmented learning, and reinforcement-based reasoning optimization methods. We discuss the symbolic foundations of logical reasoning and then analyze how neural and LLM-based methods have progressively evolved to emulate, extend, and optimize symbolic reasoning across diverse tasks. Finally, we explore emerging neurosymbolic paradigms that unify neural and symbolic reasoning to achieve interpretable, scalable, and generalizable intelligence. Our analysis underscores the growing importance of neurosymbolic AI as a foundational direction for developing reliable and explainable reasoning systems.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">5</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Rushrukh Rayan</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Samatha Ereshi Akkamahadevi</style></author><author><style face="normal" font="default" size="100%">Avishek Das</style></author><author><style face="normal" font="default" size="100%">Cara Widmer</style></author><author><style face="normal" font="default" size="100%">Eugene Y Vasserman</style></author><author><style face="normal" font="default" size="100%">Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Toward a Neurosymbolic Understanding of Hidden Neuron Activations</style></title><secondary-title><style face="normal" font="default" size="100%">Neurosymbolic Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Moe Masjedi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Decoding Grammar Perception: Identifying Specialized Neurons in LLMs</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Reza Amini</style></author><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Complex Ontology Alignment using LLMs: A Case Study</style></title><secondary-title><style face="normal" font="default" size="100%">The 20th International Workshop on Ontology Matching  collocated with the 24th International Semantic Web Conference ISWC-2025</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><pub-location><style face="normal" font="default" size="100%">Nara, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Rushrukh Rayan</style></author><author><style face="normal" font="default" size="100%">Joseph Zalewski</style></author><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Eugene Y. Vasserman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Deductive Reasoning is a Hard Deep Learning Problem</style></title><secondary-title><style face="normal" font="default" size="100%">Neurosymbolic Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Description Logic Concept Learning using Large Language Models</style></title><secondary-title><style face="normal" font="default" size="100%">19th International Conference on Neurosymbolic Learning and Reasoning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://openreview.net/forum?id=ebVC7S5VMF</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Santa Cruz, California, USA</style></pub-location><volume><style face="normal" font="default" size="100%">284:1–19, </style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Avishek Das</style></author><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hidden Neuron Activation Analysis on Labeled Text Data</style></title><secondary-title><style face="normal" font="default" size="100%">K-CAP '25: Knowledge Capture Conference 2025</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Concept-based Explanation</style></keyword><keyword><style  face="normal" font="default" size="100%">Dense Layer Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Explainable AI</style></keyword><keyword><style  face="normal" font="default" size="100%">Hidden Neuron Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2025</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">USA</style></pub-location><pages><style face="normal" font="default" size="100%">206 - 210</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding the internal mechanisms of deep neural networks remains a central challenge in the field of Explainable Artificial Intelligence (XAI). With the rapid advancement of neural architectures in natural language processing (NLP), analyzing the role of hidden neurons in capturing and processing linguistic features has become increasingly important. This study investigates Hidden Neuron Activation Analysis on labeled text data to reveal how individual neurons contribute to a model’s decision-making process. We propose a model-agnostic explainability framework for text classifiers that identifies concepts activating specific neurons involved in classification. An LSTM-based network is trained on the AG News topic classification dataset, comprising four distinct classes, and the final Dense layer with 64 neurons was analyzed. In addition, statistical analyses like the Mann-Whitney U Test is conducted to assess the robustness and reliability of the system. The statistical analysis shows that, concepts plays important role in the decision making process of neural network. Our findings enhance interpretability in NLP models and offer a foundation for optimizing neural architectures in text classification tasks.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dalal, Abhilekha</style></author><author><style face="normal" font="default" size="100%">Rayan, Rushrukh</style></author><author><style face="normal" font="default" size="100%">Barua, Adrita</style></author><author><style face="normal" font="default" size="100%">Akkamahadevi, Samatha Ereshi</style></author><author><style face="normal" font="default" size="100%">Sarker, Md Kamruzzaman</style></author><author><style face="normal" font="default" size="100%">Widmer, Cara</style></author><author><style face="normal" font="default" size="100%">Hitzler, Pascal</style></author><author><style face="normal" font="default" size="100%">Vasserman, Eugene Y</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Individual CNN Hidden-Layer Neurons Are Good Concept Encoders</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook on Neurosymbolic AI and Knowledge Graphs</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pages><style face="normal" font="default" size="100%">808–816</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge-Enhanced Geospatial QA: Integrating Wikidata Fact Verification with LLMs</style></title><secondary-title><style face="normal" font="default" size="100%">AAAI-MAKE</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Antrea Christou</style></author><author><style face="normal" font="default" size="100%">Chris Davis Jaldi</style></author><author><style face="normal" font="default" size="100%">Joseph Zalewski</style></author><author><style face="normal" font="default" size="100%">Hande Kucuk McGinty</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology for Representing Curriculum and Learning Material</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">education</style></keyword><keyword><style  face="normal" font="default" size="100%">modular ontology design</style></keyword><keyword><style  face="normal" font="default" size="100%">ontology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2506.05751</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Educational, learning, and training materials have become extremely commonplace across the Internet. Yet, they frequently remain disconnected from each other, fall into platform silos, and so on. One way to overcome this is to provide a mechanism to integrate the material and provide cross-links across topics.
In this paper, we present the Curriculum KG Ontology, which we use as a framework for the dense interlinking of educational materials, by first starting with organizational and broad pedagogical principles. We provide a materialized graph for the Prototype Open Knowledge Network use-case, and validate it using competency questions sourced from domain experts and educators. </style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Moe Masjedi</style></author><author><style face="normal" font="default" size="100%">Hossein Sholehrasa</style></author><author><style face="normal" font="default" size="100%">Samira Alkaee</style></author><author><style face="normal" font="default" size="100%">Ali Arastehfard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Primer on Deep Learning Models</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook on Neurosymbolic AI and Knowledge Graphs</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Samatha Ereshi Akkamahadevi</style></author><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automating CNN Neuron Interpretation using Concept Induction</style></title><secondary-title><style face="normal" font="default" size="100%">THE 23RD INTERNATIONAL SEMANTIC WEB CONFERENCE, ISWC 2024</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automation in AI</style></keyword><keyword><style  face="normal" font="default" size="100%">Deep Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Explainable Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge Graph</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents an automation pipeline for interpreting hidden neuron activations in Convolutional Neural Networks (CNNs), a crucial objective of Explainable AI (XAI). Previously, our research group addressed this objective by employing concept induction and semantic reasoning using a concept hierarchy derived from the Wikipedia knowledge graph. However, the process was executed manually, taking several days to complete. In this study, we have fully automated the workflow, achieving consistent results while significantly reducing the execution time. The automation pipeline streamlines model training, data preparation, concept induction, image retrieval, classification, and statistical validation, thereby completely eliminating the manual intervention. This automation enables us to efficiently interpret and validate CNN neuron activations by modifying parameters, such as incorporating a broader range of training images and classes and examining additional concept induction results across various neuron layers using different analytical tools.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joseph Zalewski</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Case for Extensional Non-Wellfounded Metamodeling</style></title><secondary-title><style face="normal" font="default" size="100%">LPAR 25</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2024</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://easychair.org/publications/paper/K8rh</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">Kalpa Publications in Computing, LPAR 25 Complementary Volume</style></edition><publisher><style face="normal" font="default" size="100%">EasyChair</style></publisher><pub-location><style face="normal" font="default" size="100%">Balaclava, Mauritius</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">163--178</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We introduce a notion of extensional metamodeling that can be used to extend knowl- edge representation languages, and show that this feature does not increase computational complexity of reasoning in many cases. We sketch the relation of our notion to various existing logics with metamodeling and to non-wellfounded sets, and discuss applications. We also comment on the usability of black-box reductions to develop reasoning algorithms for metamodeling.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andrew Eells</style></author><author><style face="normal" font="default" size="100%">Dave, B</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Commonsense Ontology Micropatterns</style></title><secondary-title><style face="normal" font="default" size="100%">18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Barcelona, ES</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrita Barua</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Concept Induction using LLMs</style></title><secondary-title><style face="normal" font="default" size="100%">The 23rd International Semantic Web Conference, ISWC 2024</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><publisher><style face="normal" font="default" size="100%"> CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Maryland, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Cara Widmer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Concept Induction using LLMs: A user experiment for assessment</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Lecture Notes in Computer Science, to appear.</style></publisher><pub-location><style face="normal" font="default" size="100%">Barcelona, Spain, September </style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Saki Norouzi, Sanaz</style></author><author><style face="normal" font="default" size="100%">Hitzler, Pascal</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DaSeLab at LLMs4OL 2024 Task A: Towards Term Typing in Ontology Learning</style></title><secondary-title><style face="normal" font="default" size="100%">The 23rd Internatinal Semantic Web Conference, ISWC 2024</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><publisher><style face="normal" font="default" size="100%">TIB Open Access Publishing proceedings</style></publisher><pub-location><style face="normal" font="default" size="100%"> Maryland, USA</style></pub-location><volume><style face="normal" font="default" size="100%">1st LLMs4OL Challenge @ ISWC 2024</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Rushrukh Rayan</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Error-margin Analysis for Hidden Neuron Activation Labels</style></title><secondary-title><style face="normal" font="default" size="100%">18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CNN</style></keyword><keyword><style  face="normal" font="default" size="100%">Concept Induction</style></keyword><keyword><style  face="normal" font="default" size="100%">Explainable AI</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer </style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Understanding how high-level concepts are represented with- in artificial neural networks is a fundamental challenge in the field of arti- ficial intelligence. While existing literature in explainable AI emphasizes the importance of labeling neurons with concepts to understand their functioning, they mostly focus on identifying what stimulus activates a neuron in most cases; this corresponds to the notion of recall in informa- tion retrieval. We argue that this is only the first-part of a two-part job; it is imperative to also investigate neuron responses to other stimuli, i.e., their precision. We call this the neuron label’s error margin.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Colby K. Fisher</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The HIP Ontology: a formal framework to support disaster risk reduction and management</style></title><secondary-title><style face="normal" font="default" size="100%">JOWO 2024 at FOIS 2024</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Daria Stepanova</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neuro-Symbolic AI and the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Abhilehka Dalal</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Evan Wallace</style></author><author><style face="normal" font="default" size="100%">Frank Riddick</style></author><author><style face="normal" font="default" size="100%">Scott Niemann</style></author><author><style face="normal" font="default" size="100%">Joe Tevis</style></author><author><style face="normal" font="default" size="100%">Farhad Ameri</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Modules for Grain Supply Chain Tracing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Antrea Christou</style></author><author><style face="normal" font="default" size="100%">Nikita Gautam</style></author><author><style face="normal" font="default" size="100%">Andrew Eells</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Population using LLMs</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook on Neurosymbolic AI and Knowledge Graphs</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hande Kucuk McGinty</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Ajay Sharda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Global Food Systems Datahub</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Saki Norouzi, Sanaz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Reza Amini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Complex Ontology Alignment using Large Language Models</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilehka Dalal</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Understanding CNN Hidden Neuron Activations using Concept Induction over Background Knowledge</style></title><secondary-title><style face="normal" font="default" size="100%">THE 23RD INTERNATIONAL SEMANTIC WEB CONFERENCE, ISWC 2024</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Concept Induction</style></keyword><keyword><style  face="normal" font="default" size="100%">Convolutional Neural Network</style></keyword><keyword><style  face="normal" font="default" size="100%">Explainable AI</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge Graph</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A major challenge in Explainable AI is interpreting hidden neuron activations accurately. These in- terpretations can reveal what a deep learning system perceives as relevant in the input data, thereby addressing the black-box nature of such systems. The state of the art indicates that hidden node acti- vations can be interpretable by humans, but there’s a lack of systematic automated methods to verify these interpretations, especially those that utilize substantial background knowledge and inherently explainable methods. In this proposal, we introduce a novel model-agnostic post-hoc Explainable AI method based on a Wikipedia-derived concept hierarchy with approximately 2 million classes. Our approach utilizes OWL-reasoning-based Concept Induction for explanation generation and compares with off-the-shelf pre-trained multimodal-based explainable methods. Our results demonstrate that our method automatically provides meaningful class expressions as explanations to individual neurons in the dense layer of a Convolutional Neural Network, outperforming prior work in both quantitative and qualitative aspects.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Understanding Hidden Neuron Activations Using Structured Background Knowledge and Deductive Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">Department of Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><volume><style face="normal" font="default" size="100%">PhD</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A central challenge in Explainable AI (XAI) is accurately interpreting hidden neuron activations in deep neural networks (DNNs). Accurate interpretations help demystify the black-box nature of deep learning models by explaining what the system internally detects as relevant in the input. While some existing methods show that hidden neuron activations can be human-interpretable, systematic and automated approaches leveraging background knowledge remain underexplored. This thesis introduces a novel model-agnostic post-hoc XAI method that integrates a Wikipedia-derived concept hierarchy of approximately 2 million classes as background knowledge and employs OWL-reasoning-based Concept Induction to generate explanations. Our approach automatically assigns meaningful class expressions to neurons in the dense layers of Convolutional Neural Networks, outperforming prior methods both quantitatively and qualitatively.&lt;/p&gt;

&lt;p&gt;In addition, we argue that understanding neuron behavior requires not only identifying what activates a neuron (recall) but also examining its precision—how it responds to other stimuli, which we define as the neuron's error margin, enhancing the granularity of neuron interpretation.&lt;/p&gt;

&lt;p&gt;To visualize these findings, we present ConceptLens, an innovative tool that visualizes neuron activations and error margins. ConceptLens offers insights into the confidence levels of neuron activations and enables an intuitive understanding of neuron behavior through visual bar charts. Together, these contributions offer a holistic approach to interpreting DNNs, advancing the explainability and transparency of AI models.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Rushrukh Rayan</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Eugene Y. Vasserman</style></author><author><style face="normal" font="default" size="100%">Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">CNN</style></keyword><keyword><style  face="normal" font="default" size="100%">Concept Induction</style></keyword><keyword><style  face="normal" font="default" size="100%">Explainable AI</style></keyword><keyword><style  face="normal" font="default" size="100%">LLM</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer </style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We introduce a novel model-agnostic post-hoc Explainable AI method that provides meaningful interpretations for hidden neuron activations in a Convolutional Neural Network. Our approach uses a Wikipedia-derived concept hierarchy with approx. 2 million classes as background knowledge, and deductive reasoning based Concept Induc- tion for explanation generation. Additionally, we explore and compare the capabilities of off-the-shelf pre-trained multimodal-based explainable methods. Our evaluation shows that our neurosymbolic method holds a competitive edge in both quantitative and qualitative aspects.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Karthik Soman</style></author><author><style face="normal" font="default" size="100%">Peter W Rose</style></author><author><style face="normal" font="default" size="100%">John H Morris</style></author><author><style face="normal" font="default" size="100%">Sergio E Baranzini</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Antrea Christou</style></author><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bridging RDF and Property Graphs: Linking KnowWhereGraph and SPOKE</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Conversational Ontology Alignment with ChatGPT</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Rushrukh Rayan</style></author><author><style face="normal" font="default" size="100%">Joseph Zalewski</style></author><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Eugene Y. Vasserman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Deductive Reasoning is a Hard Deep Learning Problem</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Ling Cai</style></author><author><style face="normal" font="default" size="100%">Yuanyuan Tian</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Kryzstof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Anna Lopez-Carr</style></author><author><style face="normal" font="default" size="100%">Andrew Schroeder</style></author><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Zilong Liu</style></author><author><style face="normal" font="default" size="100%">Colby Fisher</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expertise Ontology: Modeling Expertise in the Context of Emergency Management</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Joint Ontology Workshops 2023 Episode IX: The Quebec Summer of Ontology co-located with the 13th International Conference on Formal Ontology in Information Systems (FOIS 2023)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR Workshop Proceedings 3637, CEUR-WS.org 2023.</style></publisher><pub-location><style face="normal" font="default" size="100%">Sherbrooke, Quebec, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Explaining Deep Learning Hidden Neuron Activations using Concept Induction</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;One of the current key challenges in Explainable AI is in correctly interpreting activations of hidden neurons. It seems evident that accurate interpretations thereof would provide insights into the question what a deep learning system has internally detected as relevant on the input, thus lifting some of the black box character of deep learning systems.&lt;/p&gt;

&lt;p&gt;The state of the art on this front indicates that hidden node activations appear to be interpretable in a way that makes sense to humans, at least in some cases. Yet, systematic automated methods that would be able to first hypothesize an interpretation of hidden neuron activations, and then verify it, are mostly missing.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;In this paper, we provide such a method and demonstrate that it provides meaningful interpretations. It is based on using large-scale background knowledge -- a class hierarchy of approx. 2 million classes curated from the Wikipedia Concept Hierarchy -- together with a symbolic reasoning approach called concept induction&amp;nbsp;based on description logics that was originally developed for applications in the Semantic Web field.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Our results show that we can automatically attach meaningful labels from the background knowledge to individual neurons in the dense layer of a Convolutional Neural Network through a hypothesis and verification process.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Meilin Shi</style></author><author><style face="normal" font="default" size="100%">Colby K. Fisher</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Zilong Liu</style></author><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast Forward from Data to Insight: (Geographic) Knowledge Graphs and Their Applications. </style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Geospatial Artificial Intelligence,CRC Press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><edition><style face="normal" font="default" size="100%">In: Song Gao, Yinjie Hu, Wenwen Li (eds.)</style></edition><pages><style face="normal" font="default" size="100%">411-426</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Formal Framework for Disaster Risk Properties</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Joint Ontology Workshops 2023 Episode IX: The Quebec Summer of Ontology co-located with the 13th International Conference on Formal Ontology in Information Systems (FOIS 2023)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR Workshop Proceedings 3637,CEUR-WS.org 2023.</style></publisher><pub-location><style face="normal" font="default" size="100%">Sherbrooke, Quebec, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Ling Cai</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Zhangyu Wang</style></author><author><style face="normal" font="default" size="100%">Yuanyuan Tian</style></author><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Zilong Liu</style></author><author><style face="normal" font="default" size="100%">Meilin Shi</style></author><author><style face="normal" font="default" size="100%">Colby K. Fisher</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The KnowWhereGraph Ontology</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Colby Fisher</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Antrea Christou</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Abhilehka Dalal</style></author><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Yuanyuan Tian</style></author><author><style face="normal" font="default" size="100%">Ling Cai</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Zhangyu Wang</style></author><author><style face="normal" font="default" size="100%">Zilong Liu</style></author><author><style face="normal" font="default" size="100%">Meilin Shi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The KnowWhereGraph Ontology: A Showcase</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Joint Ontology Workshops 2023 Episode IX: The Quebec Summer of Ontology co-located with the 13th International Conference on Formal Ontology in Information Systems (FOIS 2023),</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2023</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">CEUR Workshop Proceedings 3637, CEUR-WS.org 2023.</style></publisher><pub-location><style face="normal" font="default" size="100%">Sherbrooke, Quebec, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Colby K. Fisher</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Antrea Christou</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Zilong Liu</style></author><author><style face="normal" font="default" size="100%">Meilin Shi</style></author><author><style face="normal" font="default" size="100%">Ling Cai</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Zhangyu Wang</style></author><author><style face="normal" font="default" size="100%">Yuanyuan Tian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The KnowWhereGraph Ontology: A Showcase</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Antrea Christou</style></author><author><style face="normal" font="default" size="100%">Kitty Currier</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Sanaz Saki Norouzi</style></author><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Adrita Barua</style></author><author><style face="normal" font="default" size="100%">Colby K. Fisher</style></author><author><style face="normal" font="default" size="100%">Anthony D’Onofrio</style></author><author><style face="normal" font="default" size="100%">Thomas Thelen</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">KnowWhereGraph-Lite: A Perspective of the KnowWhereGraph</style></title><secondary-title><style face="normal" font="default" size="100%">KGSWC 2023</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rushrukh Rayan</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MMODS-O: A Modular Ontology for the Metadata Object Description Schema (MODS) – Documentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We are presenting the documentation for MMODS-O, an ontology derived from the Metadata Object Description Schema (MODS, version 3.8), which is an XML Schema by The Library of Congress. The XML Schema concerns metadata pertaining to bibliographic elements, however it is also used for other purposes, for instance LCACommons which is an interagency community that focues on Life Cycle Analysis, National Agricultural Library -- require the metadata to be in MODS format. &amp;nbsp;Our motivation for developing this ontology -- including how it relates to previous attempts -- will be described elsewhere. This documentation is intended for readers who are familiar with MODS XML schema.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rushrukh Rayan</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Heidi Sieverding</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Modular Ontology for MODS – Metadata Object Description Schema</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Metadata Object Description Schema (MODS) was developed to describe bibliographic concepts and metadata and is maintained by the Library of Congress. Its authoritative version is given as an XML schema based on an XML mindset which means that it has significant limitations for use in a knowledge graphs context. We have therefore developed the Modular MODS Ontology (MMODS-O) which incorporates all elements and attributes of the MODS XML schema. In designing the ontology, we adopt the recent Modular Ontology Design Methodology (MOMo) with the intention to strike a balance between modularity and quality ontology design on the one hand, and conservative backward compatibility with MODS on the other.&amp;nbsp;&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aonty, Shuhena Salam</style></author><author><style face="normal" font="default" size="100%">Deb, Kaushik</style></author><author><style face="normal" font="default" size="100%">Sarma, Moumita Sen</style></author><author><style face="normal" font="default" size="100%">Dhar, Pranab Kumar</style></author><author><style face="normal" font="default" size="100%">Shimamura, Tetsuya</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biological system modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">bottom-up parsing</style></keyword><keyword><style  face="normal" font="default" size="100%">Convolutional Neural Network</style></keyword><keyword><style  face="normal" font="default" size="100%">Convolutional neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Feature extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Location awareness</style></keyword><keyword><style  face="normal" font="default" size="100%">occlusion</style></keyword><keyword><style  face="normal" font="default" size="100%">Pose estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">skeletal keypoint</style></keyword><keyword><style  face="normal" font="default" size="100%">Solid modeling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">42343-42360</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rushrukh Rayan</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Role-Dependent Names</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;page&quot; title=&quot;Page 1&quot;&gt;
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&lt;p&gt;&lt;span&gt;We present an ontology design pattern for modeling&amp;nbsp;&lt;/span&gt;&lt;span&gt;Names&amp;nbsp;&lt;/span&gt;&lt;span&gt;as part of&amp;nbsp;&lt;/span&gt;&lt;span&gt;Roles&lt;/span&gt;&lt;span&gt;, to capture scenarios where an&amp;nbsp;&lt;/span&gt;&lt;span&gt;Agent&amp;nbsp;&lt;/span&gt;&lt;span&gt;performs different&amp;nbsp;&lt;/span&gt;&lt;span&gt;Roles&amp;nbsp;&lt;/span&gt;&lt;span&gt;using different&amp;nbsp;&lt;/span&gt;&lt;span&gt;Names&amp;nbsp;&lt;/span&gt;&lt;span&gt;associated with the different Roles. Examples of an Agent performing a Role using different Names are rather ubiq- uitous, e.g., authors who write under different pseudonyms, or different legal names for citizens of more than one country. The proposed pattern is a modified merger of a standard Agent Role and a standard Name pattern stub.&lt;/span&gt;&lt;/p&gt;
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Lecture Notes in Computer Science 14266, Springer 2023</style></volume><pages><style face="normal" font="default" size="100%">419-434</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Ling Cai</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Zilong Liu</style></author><author><style face="normal" font="default" size="100%">Zhangyu Wang</style></author><author><style face="normal" font="default" size="100%">Meilin Shi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Diverse data! Diverse schemata?</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.3233/SW-210453</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">1–3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carral, David</style></author><author><style face="normal" font="default" size="100%">Zalewski, Joseph</style></author><author><style face="normal" font="default" size="100%">Hitzler, Pascal</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An efficient algorithm for reasoning over OWL EL ontologies with nominal schemas</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Logic and Computation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1093/logcom/exac032</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Nominal schemas have been proposed as an extension to Description Logics (DL), the knowledge representation paradigm underlying the Web Ontology Language (OWL). They provide for a very tight integration of DL and rules. Nominal schemas can be understood as syntactic sugar on top of OWL. However, this naive perspective leads to inefficient reasoning procedures. In order to develop an efficient reasoning procedure for the language \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}\\mathcal \\{V\\}^\\{++\\}\\}\\$, which results from extending the OWL profile language OWL EL with nominal schemas, we propose a transformation from \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}\\mathcal \\{V\\}^\\{++\\}\\}\\$ ontologies into Datalog-like rule programs that can be used for satisfiability checking and assertion retrieval. The use of this transformation enables the use of powerful Datalog engines to solve reasoning tasks over \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}\\mathcal \\{V\\}^\\{++\\}\\}\\$ ontologies. We implement and then evaluate our approach on several real-world, data-intensive ontologies, and find that it can outperform state-of-the-art reasoners such as Konclude and ELK. As a lesser side result we also provide a self-contained description of a rule-based algorithm for \\$\\{\\mathcal \\{E\\}\\mathcal \\{L\\}^\\{++\\}\\}\\$, which does not require a normal form transformation.}&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nariman Ammar</style></author><author><style face="normal" font="default" size="100%">Tom Carlson</style></author><author><style face="normal" font="default" size="100%">Kevin Doubleday</style></author><author><style face="normal" font="default" size="100%">Katherine Escobar</style></author><author><style face="normal" font="default" size="100%">Nat Fuller</style></author><author><style face="normal" font="default" size="100%">Michael Grove</style></author><author><style face="normal" font="default" size="100%">Brian Handspicker</style></author><author><style face="normal" font="default" size="100%">Dave Hardy</style></author><author><style face="normal" font="default" size="100%">Jeff Heflin</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Sharat Israni</style></author><author><style face="normal" font="default" size="100%">Eric Jahn</style></author><author><style face="normal" font="default" size="100%">Ora Lassila</style></author><author><style face="normal" font="default" size="100%">Timothy Lebo</style></author><author><style face="normal" font="default" size="100%">Chengkai Li</style></author><author><style face="normal" font="default" size="100%">Christina Medlin</style></author><author><style face="normal" font="default" size="100%">Art Murray</style></author><author><style face="normal" font="default" size="100%">Ron Rudnicki</style></author><author><style face="normal" font="default" size="100%">Lauren Sanders</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Greg Seaton</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Karthik Soman</style></author><author><style face="normal" font="default" size="100%">Paul Wormeli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Group D: NIEM Ontology</style></title><secondary-title><style face="normal" font="default" size="100%">Open Knowledge Network Roadmap: Appendix A: Use Cases.NSF’s Conergence Accelerator, </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2022</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">31-40</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Sudip Bhattacharjee</style></author><author><style face="normal" font="default" size="100%">John MacMullen</style></author><author><style face="normal" font="default" size="100%">Shashi Shekhar</style></author><author><style face="normal" font="default" size="100%">Ann Stapleton</style></author><author><style face="normal" font="default" size="100%">Ilya Zaslavsky</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Douglas Rao</style></author><author><style face="normal" font="default" size="100%">Matthew Lange</style></author><author><style face="normal" font="default" size="100%">Sharat Israni</style></author><author><style face="normal" font="default" size="100%">Ellie Young</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Group E: Connecting investments in nutrition security and climate</style></title><secondary-title><style face="normal" font="default" size="100%">Open Knowledge Network Roadmap: Appendix A: Use Cases. 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version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andrew Eells</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Seila Gonzalez Estrecha</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aligning Patterns to the Wikibase Model</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Ontology Design and Patterns</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatically Generating Human Readable Documentation for Ontology Design Patterns</style></title><secondary-title><style face="normal" font="default" size="100%">International Semantic Web Conference Poster and Demos</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>9</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatically Generating Human Readable Documentation for Ontology Design Patterns</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sen Sarma, Moumita</style></author><author><style face="normal" font="default" size="100%">Das, Avishek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BMGC: A Deep Learning Approach to Classify Bengali Music Genres</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 4th International Conference on Networking, Information Systems &amp; Security</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bengali music classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Bengali Music Dataset.</style></keyword><keyword><style  face="normal" font="default" size="100%">Deep Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Gated Recurrent Unit (GRU)</style></keyword><keyword><style  face="normal" font="default" size="100%">Mel Frequency Cepstral Coefficient (MFCC)</style></keyword><keyword><style  face="normal" font="default" size="100%">Recurrent Neural Network</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1145/3454127.3456593</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Association for Computing Machinery</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><isbn><style face="normal" font="default" size="100%">9781450388719</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Music genre classification (MGC) is the process of tagging music with their appropriate genres by analyzing music signals or the lyrics. With the accelerated surge in music data repositories, MGC can be extensively used in music recommendation systems, advertisement, and streaming services for systematic and efficient management. However, there have been many works on English music classification using different statistical and machine learning approaches, but there is no notable progress found in the arena of Bengali music. Besides, a few significant works have been found in utilizing Deep Learning (DL) methods to classify different music genres. Bengali music is significantly enriched with its contents and uniqueness. Moreover, the extent and scope of exploring the DL approach in Bengali music ground are still latent. Therefore, Bengali music genre classification is quite a new research area in the Deep learning field. In this work, we have constructed a Bengali Music Genre Classifier (BMGC) to categorize 6 Bengali music genres: ‘Adhunik’, ‘Band’, ‘Hiphop’, ‘Nazrulgeeti’, ‘Lalon’, and ‘Rabindra Sangeet’. We have created a Bengali music genre classification dataset (hereafter named BMGCD) containing 2944 Bengali music clips, and a Gated Recurrent Unit based deep learning model has been developed to predict the music genre from audio signals. Our developed model achieved an accuracy of 80.4% and 80.6% F1-score which surpasses the related existing works.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bridging Upper Ontology and Modular Ontology Modeling: A Tool and Evaluation</style></title><secondary-title><style face="normal" font="default" size="100%">KGSWC-2021</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Ontologies are increasingly used as schema for knowledge graphs in many application areas. As such, there are a variety of different approaches for their development. In this paper, we describe and evaluate UAO (for Upper Ontology Alignment Tool), which is an extension to CoModIDE, a graphical prote'ge'&amp;nbsp;plugin for modular ontology modeling. UAO enables ontology engineers to combine modular ontology modeling with a more traditional ontology modeling approach based on upper ontologies. We posit -- and our evaluation supports this claim -- that the tool does indeed makes it easier to combine both approaches. Thus, UAO enables a best-of-both-worlds approach. The evaluation consists of a user study, and the results show that performing typical manual alignment modeling tasks is relatively easier with UAO than doing it with porte'ge' alone, in terms of the time required to complete the task and improving the correctness of the output. Additionally, our test subjects provided significantly higher ratings on the System Utilization Scale for UOA.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>36</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Capabilities of Pointer Networks for Deep Deductive Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><num-vols><style face="normal" font="default" size="100%">arXiv:2106.09225</style></num-vols></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Shirly Stephen Ambrose</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Ling Cai</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Environmental Observations in Knowledge Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">DaMaLOS 2021 @ ISWC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The notion of Linked Open Science rests on the assumption that Linked Data principles contribute to science and scientific data management in several distinct ways (e.g., by adding rich semantics to improve retrieval and reuse of data). This begs the question of the right level of granularity for such semantic enrichment. On the one extreme of the spectrum, one may provide semantic annotations on the level of entire datasets to improve retrieval while leaving the actual data untouched. On the other end, one may semantically describe every single datum, such as a particular observation leading to data that supports reasoning, automated conflation, and so on, while, at the same time, dramatically increasing the size of data, including redundancy. This paper reports on our experience in modeling heterogeneous environmental data using a semantically-enabled observation framework, namely the SOSA ontology and its extensions to handle observation collections. We discuss different means of using these observation collections and compare their pros and cons in terms of data size and ease of querying.&amp;nbsp;&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Cogan M Shimizu</style></author><author><style face="normal" font="default" size="100%">Sulogna Chowdhury</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expressibility of OWL Axioms with Patterns</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web – 18th International Conference, ESWC 2021</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pages><style face="normal" font="default" size="100%">230-245</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Sulogna Chowdhury</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expressibility of OWL Axioms with Patterns</style></title><secondary-title><style face="normal" font="default" size="100%">ESWC 2021</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The high expressivity of the Web Ontology Language (OWL) makes it possible to describe complex relationships between classes, roles, and individuals in an ontology. However, this high expressivity can be an obstacle to correct usage and wide adoption. Past attempts to ameliorate this have included the development of specific, presumably human-friendly syntaxes, such as the Manchester syntax or graphical interfaces for OWL axioms, albeit with limited success. If modelers want to develop suitable OWL axioms it is important to make this as easy as possible. In this paper, we adopt an idea from the Protégé plug-in, OWLAx, which provides a simple, clickable interface to automatically input axioms of a limited number of types by following simple axiom patterns. In particular, each of these axiom patterns contains at most three classes or roles. We hypothesize that most of the axioms in existing ontologies could be expressed semantically in terms of simple patterns like these, which would mean that more complex patterns can be used very sparingly. Our findings, based on an analysis of 518 ontologies from six public ontology repositories, confirm this hypothesis: Over 90% of class axioms in the average ontology are indeed expressible with our simple patterns. We provide a detailed analysis of our findings.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joseph Zalewski</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">InK Browser - The Interactive Knowledge Browser</style></title><secondary-title><style face="normal" font="default" size="100%">International Semantic Web Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">20</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present an improved implementation of the Interactive Knowledge Browser (InK Browser), a tool for exploring knowledge graphs visually, using a schema diagram.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Karl Hammar</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modular Ontology Modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Federico Bianchi</style></author><author><style face="normal" font="default" size="100%">Ning Xie</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Derek Doran</style></author><author><style face="normal" font="default" size="100%">HyeongSik Kim</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment</style></title><secondary-title><style face="normal" font="default" size="100%">AAAI-MAKE 2021</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">AAAI</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Andrew Eells</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Open Science  data and the Semantic Web journal</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12(3)</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Editorial</style></work-type><section><style face="normal" font="default" size="100%">401-402</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Pattern for Features on a Hierarchical Spatial Grid</style></title><secondary-title><style face="normal" font="default" size="100%">The 10th International Joint Conference on Knowledge Graphs, IJCKG 2021, December 6-8, 2021, Virtual Event, Thailand</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Genchen Mai</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Pattern for Modeling Causal Relations Between Events</style></title><secondary-title><style face="normal" font="default" size="100%">13th Workshop on Ontology Design and Patterns</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mina Abd Nikooie Pour</style></author><author><style face="normal" font="default" size="100%">Alsayed Algergawy</style></author><author><style face="normal" font="default" size="100%">Florence Amardeilh</style></author><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Omaima Fallatah</style></author><author><style face="normal" font="default" size="100%">Daniel Faria</style></author><author><style face="normal" font="default" size="100%">Irini Fundulaki</style></author><author><style face="normal" font="default" size="100%">Ian Harrow</style></author><author><style face="normal" font="default" size="100%">Sven Hertling</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Martin Huschka</style></author><author><style face="normal" font="default" size="100%">Liliana Ibanescu</style></author><author><style face="normal" font="default" size="100%">Ernesto Jimenez- Ruiz</style></author><author><style face="normal" font="default" size="100%">Naouel Karam</style></author><author><style face="normal" font="default" size="100%">Amir Laadhar</style></author><author><style face="normal" font="default" size="100%">Patrick Lambrix</style></author><author><style face="normal" font="default" size="100%">Huanyu Li</style></author><author><style face="normal" font="default" size="100%">Ying Li</style></author><author><style face="normal" font="default" size="100%">Franck Michel</style></author><author><style face="normal" font="default" size="100%">Engy Nasr</style></author><author><style face="normal" font="default" size="100%">Heiko Paulheim</style></author><author><style face="normal" font="default" size="100%">Catia Pesquita</style></author><author><style face="normal" font="default" size="100%">Jan Portisch</style></author><author><style face="normal" font="default" size="100%">Catherine Roussey</style></author><author><style face="normal" font="default" size="100%">Tzanina Saveta</style></author><author><style face="normal" font="default" size="100%">Pavel Shvaiko</style></author><author><style face="normal" font="default" size="100%">Andrea Splendiani</style></author><author><style face="normal" font="default" size="100%">Cassia Trojahn</style></author><author><style face="normal" font="default" size="100%">Jana Vatascinova</style></author><author><style face="normal" font="default" size="100%">Beyza Yaman</style></author><author><style face="normal" font="default" size="100%">Ondrej Zamazal</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Results of the Ontology Alignment Evaluation Initiative 2021</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 16th International Workshop on Ontology Matching co-located with the 20th International Semantic Web Conference (ISWC 2021)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2021</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Review of the Semantic Web Field</style></title><secondary-title><style face="normal" font="default" size="100%">Communications of the ACM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2021</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">64</style></volume><pages><style face="normal" font="default" size="100%">76-83</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">76</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Hilmar Lapp</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Seed Patterns for Modeling Trees</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Pattern-Based Ontology Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pages><style face="normal" font="default" size="100%">48-67</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Trees – i.e., the type of data structure known under this name – are central to many aspects of knowledge organization. We investigate some central design choices concerning the ontological modeling of such trees. In particular, we consider the limits of what is expressible in the Web Ontology Language and provide a reusable ontology design pattern for trees.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zalewski, Joseph</style></author><author><style face="normal" font="default" size="100%">Hitzler, Pascal</style></author><author><style face="normal" font="default" size="100%">Janowicz, Krzysztof</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Compression with Region Calculi in Nested Hierarchical Grids</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 29th International Conference on Advances in Geographic Information Systems</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Hierarchical Grids</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge Graphs</style></keyword><keyword><style  face="normal" font="default" size="100%">RCC5</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1145/3474717.3483965</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Association for Computing Machinery</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><pages><style face="normal" font="default" size="100%">305–308</style></pages><isbn><style face="normal" font="default" size="100%">9781450386647</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joseph Zalewski</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Compression with Region Calculi in Nested Hierarchical Grids (Technical Report)</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Hierarchical Grids</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge Graphs</style></keyword><keyword><style  face="normal" font="default" size="100%">RCC5</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rui Zhu</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Shirly Stephen</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Ling Cai</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology</style></title><secondary-title><style face="normal" font="default" size="100%">The 10th International Joint Conference on Knowledge Graphs, IJCKG 2021, December 6-8, 2021, Virtual Event, Thailand</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Colin Kupitz</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Daniel Schmidt</style></author><author><style face="normal" font="default" size="100%">Christopher Stevens</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Dario Salvucci</style></author><author><style face="normal" font="default" size="100%">Benji Maruyama</style></author><author><style face="normal" font="default" size="100%">Chris Myers</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Toward Undifferentiated Cognitive Models</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Cognitive Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><edition><style face="normal" font="default" size="100%">19</style></edition><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Autonomous systems are a new frontier for pushing sociotechnical advancement. Such systems will eventually become pervasive, involved in everything from manufacturing, healthcare, defense, and even research itself. However, proliferation is stifled by the high development costs and the resulting inflexibility of the produced systems. The current time needed to create and integrate state of the art autonomous systems that operate as team members in complex situations is a 3-15 year development period, often requiring humans to adapt to limitations in the resulting systems. A new research thrust in interactive task learning (ITL) has begun, calling for natural human-autonomy interaction to facilitate system flexibility and minimize users’ complexity in providing autonomous systems with new tasks. We discuss the development of an undifferentiated agent with a modular framework as a method of approaching that goal.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Ryan McGranaghan</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Adam C. Kellerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Modular Ontology for Space Weather Research</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Ontology Design Patterns</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Federico Bianchi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Bridging the Neuro-Symbolic Gap: Deep Deductive Reasoners</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards generalizable neuro-symbolic reasoners</style></title><secondary-title><style face="normal" font="default" size="100%">Department of Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://krex.k-state.edu/dspace/handle/2097/41621</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Kansas State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Manhattan, KS</style></pub-location><volume><style face="normal" font="default" size="100%">PhD</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Dissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sarma, Moumita Sen</style></author><author><style face="normal" font="default" size="100%">Deb, Kaushik</style></author><author><style face="normal" font="default" size="100%">Dhar, Pranab Kumar</style></author><author><style face="normal" font="default" size="100%">Koshiba, Takeshi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Traditional Bangladeshi Sports Video Classification Using Deep Learning Method</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2076-3417/11/5/2149</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal features from the videos. In this regard, a new Traditional Bangladeshi Sports Video (TBSV) dataset is constructed containing five classes: Boli Khela, Kabaddi, Lathi Khela, Kho Kho, and Nouka Baich. A key contribution of this paper is to develop a scratch model by incorporating the two most prominent deep learning algorithms: convolutional neural network (CNN) and long short term memory (LSTM). Moreover, the transfer learning approach with the fine-tuned VGG19 and LSTM is used for TBSV classification. Furthermore, the proposed model is assessed over four challenging datasets: KTH, UCF-11, UCF-101, and UCF Sports. This model outperforms some recent works on these datasets while showing 99% average accuracy on the TBSV dataset.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uddin, Asif Mahbub</style></author><author><style face="normal" font="default" size="100%">Al Miraj, Abdullah</style></author><author><style face="normal" font="default" size="100%">Sen Sarma, Moumita</style></author><author><style face="normal" font="default" size="100%">Das, Avishek</style></author><author><style face="normal" font="default" size="100%">Gani, Md. Manjurul</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Traditional Bengali Food Classification Using Convolutional Neural Network</style></title><secondary-title><style face="normal" font="default" size="100%">2021 IEEE Region 10 Symposium (TENSYMP)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Convolutional Neural Network</style></keyword><keyword><style  face="normal" font="default" size="100%">Convolutional neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Data models</style></keyword><keyword><style  face="normal" font="default" size="100%">fine tuning</style></keyword><keyword><style  face="normal" font="default" size="100%">image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Image recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">traditional Bengali foods</style></keyword><keyword><style  face="normal" font="default" size="100%">Transfer learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Turning</style></keyword><keyword><style  face="normal" font="default" size="100%">VGG16</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pages><style face="normal" font="default" size="100%">1-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Advances in modular ontology engineering: methodology and infrastructure</style></title><secondary-title><style face="normal" font="default" size="100%">Department of Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2020</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Kansas State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Manhattan</style></pub-location><volume><style face="normal" font="default" size="100%">Ph.D.</style></volume><pages><style face="normal" font="default" size="100%">175</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modular ontology engineering is a methodology for producing highly reusable knowledge graph schema. Over the course of this dissertation, we outline a number of contributions that have improved the process to what we see today. These contributions fall within four categories: conveying meaning through schema diagrams, the composition of a modular ontology, the modular ontology engineering methodology, and modular graphical modeling.&lt;/p&gt;

&lt;p&gt;First, we created an improved method and tool for generating schema diagrams similar to those manually generated by humans and show that most of OWL, as it is used in real world ontologies, are expressible in this format.&lt;/p&gt;

&lt;p&gt;Next, we examined and improved the ontology design pattern development process. This was accomplished through the development of both patterns and modules, extensions to the ontology design pattern representation language, and a tool that significantly improves the usability of these annotations. This work culminated in MODL: a modular ontology design library, which is a distributable set of curated, well-documented ODPs, both novel and drawn from the ontology design pattern portal.&lt;/p&gt;

&lt;p&gt;These advances were combined, and building upon the state of the art, to create the Comprehensive Modular Ontology Design IDE (CoModIDE), which is a plugin for the industry-standard ontology editor, Protege.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;Finally, as a culmination of the tool and the methodology, we evaluated CoModIDE, where it was shown to significantly improve outcomes for experienced and new ontology developers when developing modular ontologies.&lt;/p&gt;

&lt;p&gt;Altogether, these research topics, resulted in a methodology, that when executed, produced actually reusable, extendable, and adaptable ontologies.&amp;nbsp;&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Cumulative</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AROA Results of OAEI 2020</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Matching</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2020</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Completion Reasoning Emulation for the Description Logic EL+</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Deep Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">EL+</style></keyword><keyword><style  face="normal" font="default" size="100%">LSTM</style></keyword><keyword><style  face="normal" font="default" size="100%">NeSy</style></keyword><keyword><style  face="normal" font="default" size="100%">Reasoning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-2600/paper5.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Stanford University, Palo Alto, California, USA</style></pub-location><volume><style face="normal" font="default" size="100%">2600</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be inspected part-way through, rather than simply learning reasoner answers, which is typical in many of the black-box systems currently in use. We demonstrate that this idea is feasible by training a long short-term memory (LSTM) artificial neural network to learn EL+ reasoning patterns with two different data sets. We also show that this trained system is resistant to noise by corrupting a percentage of the test data and comparing the reasoner's and LSTM's predictions on corrupt data with correct answers.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ion Juvina</style></author><author><style face="normal" font="default" size="100%">William R. Aue</style></author><author><style face="normal" font="default" size="100%">Brandon Minnery</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Srikanth Nadella</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Counterfactual reasoning over large-scale human performance optimization experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Virtual poster presented at the annual meeting of the Psychonomic Society, November 2020</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mehwish Alam</style></author><author><style face="normal" font="default" size="100%">Paul Groth</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Heiko Paulheim</style></author><author><style face="normal" font="default" size="100%">Harald Sack</style></author><author><style face="normal" font="default" size="100%">Volker Tresp</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CSSA'20: Workshop on Combining Symbolic and Sub-Symbolic Methods and their Applications</style></title><secondary-title><style face="normal" font="default" size="100%">CIKM'20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">3523-3524</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Ryan McGranaghan</style></author><author><style face="normal" font="default" size="100%">Adam C. Kellerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Integration with Knowledge Graphs: A Space Weather Use-case</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><pub-location><style face="normal" font="default" size="100%">American Geophysical Union (AGU) Fall Meeting 2020</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Christopher Stevens</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Christopher W. Myers</style></author><author><style face="normal" font="default" size="100%">Benji Maruyam</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Domain Ontology for Task Instructions</style></title><secondary-title><style face="normal" font="default" size="100%">KGSWC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%"> Knowledge graphs and ontologies represent information in a variety of different applications. One use case, the Intelligence, Surveillance, &amp; Reconnaissance: Mutli-Attribute Task Battery (ISR-MATB), comes from Cognitive Science, where researchers use interdisciplinary methods to understand the mind and cognition. The ISR-MATB is a set of tasks that a cognitive or human agent perform which test visual, 
 auditory, and memory capabilities. An ontology can represent a cognitive agent’s background knowledge of the task it was instructed to perform and act as an interchange format between different Cognitive Agent tasks similar to ISR-MATB. We present several modular patterns for representing ISR-MATB task instructions, as well as a unified diagram that links them together.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Alicia M Sheill</style></author><author><style face="normal" font="default" size="100%">Seila Gonzalez Estrecha</style></author><author><style face="normal" font="default" size="100%">Catherine Foley</style></author><author><style face="normal" font="default" size="100%">Duncan Tarr</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Enslaved Dataset: A Real-world Complex Ontology Alignment Benchmark using Wikibase</style></title><secondary-title><style face="normal" font="default" size="100%">29th ACM International Conference on Information and Knowledge Management</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2020</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Quinn Hirt</style></author><author><style face="normal" font="default" size="100%">Dean Rehberger</style></author><author><style face="normal" font="default" size="100%">Seila Gonzalez Estrecha</style></author><author><style face="normal" font="default" size="100%">Catherine Foley</style></author><author><style face="normal" font="default" size="100%">Alicia M. Sheill</style></author><author><style face="normal" font="default" size="100%">Walter Hawthorne</style></author><author><style face="normal" font="default" size="100%">Jeff Mixter</style></author><author><style face="normal" font="default" size="100%">Ethan Watrall</style></author><author><style face="normal" font="default" size="100%">Ryan Carty</style></author><author><style face="normal" font="default" size="100%">Duncan Tarr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Enslaved Ontology: Peoples of the Historic Slave Trade</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Web Semantics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data integration</style></keyword><keyword><style  face="normal" font="default" size="100%">digital humanities</style></keyword><keyword><style  face="normal" font="default" size="100%">history of the slave trade</style></keyword><keyword><style  face="normal" font="default" size="100%">modular ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Design Patterns</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">63</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present the Enslaved Ontology (V1.0) which was developed for integrating data about the historic slave trade from diverse sources in a use case driven by historians. Ontology development followed modular ontology design principles as derived from ontology design pattern application best practices and the eXtreme Design Methodology. Ontology content focuses on data about historic persons and the event records from which this data can be taken. It also incorporates provenance modeling and some temporal and spatial aspects. The ontology is available as serialized in the Web Ontology Language OWL, and carries modularization annotations using the Ontology Pattern Language (OPLa). It is available under the Creative Commons CC BY 4.0 license.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zachary A. Daniels</style></author><author><style face="normal" font="default" size="100%">Logan D. Frank</style></author><author><style face="normal" font="default" size="100%">Christopher J. Menart</style></author><author><style face="normal" font="default" size="100%">Michael Raymer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Framework for Explainable Deep Neural Models Using External Knowledge Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, SPIE</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Functional API for OWL</style></title><secondary-title><style face="normal" font="default" size="100%">The 19th International Semantic Web Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">2721</style></volume><abstract><style face="normal" font="default" size="100%">We present (f OWL), a minimalistic, functional programming style ontology editor that is based directly on the OWL 2 Structural Specification. (f OWL) is written from scratch, entirely in Clojure, having no other dependencies. Ontologies in (f OWL) are implemented as standalone and homogeneous data structures, which means that the same exact functions written for single axioms or expressions often work identically on any part of an ontology, even the entire ontology itself. The lazy functional style of Clojure also allows for intuitive and simple ontology creation and modification with a minimal memory footprint. All of this is possible without ever needing to use a single class, except of course in the Ontologies one creates!</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GeoLink Cruises: A Non-Synthetic Benchmark for Co-Reference Resolution on Knowledge Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">International conference on information and knowledge management</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2020</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">ACM DL</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GeoLink Dataset: A Complex Alignment Benchmark from Real-world Ontology</style></title><secondary-title><style face="normal" font="default" size="100%">Data Intelligence </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gold-Level Open Access at the Semantic Web Journal</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.semantic-web-journal.net/content/gold-level-open-access-semantic-web-journal</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ilaria Tiddi</style></author><author><style face="normal" font="default" size="100%">Freddy Lécué</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges</style></title><secondary-title><style face="normal" font="default" size="100%">Studies on the Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.iospress.nl/book/knowledge-graphs-for-explainable-artificial-intelligence-foundations-applications-and-challenges/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><volume><style face="normal" font="default" size="100%">47</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Karl Hammar</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Andreas Harth</style></author><author><style face="normal" font="default" size="100%">Sabrina Kirrane</style></author><author><style face="normal" font="default" size="100%">Axel-Cyrille Ngonga Ngomo</style></author><author><style face="normal" font="default" size="100%">Heiko Paulheim</style></author><author><style face="normal" font="default" size="100%">Anisa Rula</style></author><author><style face="normal" font="default" size="100%">Anna Lisa Gentile</style></author><author><style face="normal" font="default" size="100%">Peter Haase</style></author><author><style face="normal" font="default" size="100%">Michael Cochez</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Modular Graphical Ontology Engineering Evaluated</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-030-49461-2\2</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">12123</style></volume><pages><style face="normal" font="default" size="100%">20–35</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modular Ontology Modeling: A Tutorial</style></title><secondary-title><style face="normal" font="default" size="100%">Applications and Practices in Ontology Design, Extraction, and Reasoning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><volume><style face="normal" font="default" size="100%">49</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We provide an in-depth example of modular ontology engineering with ontology design patterns. The style and content of this chapter is adapted from previous work and tutorials on Modular Ontology Modeling. It o ers expanded steps and updated tool information. The tutorial is largely self-contained, but assumes that the reader is familiar with the Web Ontology Language OWL; however, we do briefly review some foundational concepts. By the end of the tutorial, we expect&lt;br /&gt;
the reader to have an understanding of the underlying motivation and methodology for producing a modular ontology.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">1</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modular Ontology Modeling Meets Upper Ontologies:  The Upper Ontology Alignment Tool</style></title><secondary-title><style face="normal" font="default" size="100%">The 19th International Semantic Web Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">2721</style></volume><pages><style face="normal" font="default" size="100%">119-124</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We provide an extension to the Prote'ge'-based modular&amp;nbsp;ontology engineering tool CoModIDE, in order to make it possible for ontology engineers to adhere to traditional ontology modeling processes based on upper or foundational ontologies. As a bridge between the more recently proposed modular ontology modeling approach and more classical ones based on foundational ontologies, it enables a best-of-both-worlds approach for ontology engineering.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modular Ontology Modeling Meets Upper Ontologies:  The Upper Ontology Alignment Tool</style></title><secondary-title><style face="normal" font="default" size="100%">Department of Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2020</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Kansas State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Manhattan</style></pub-location><volume><style face="normal" font="default" size="100%">Masters</style></volume><pages><style face="normal" font="default" size="100%">35</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Ontology modeling has become a primary approach to schema generation for data integration and knowledge graphs in many application areas. The quest for efficient approaches to model useful and re-useable ontologies has led to different ontology creation proposals over the years. The project focuses on two major approaches, modeling using a top-level ontology, and the other is modular ontology modeling.&lt;/p&gt;

&lt;p&gt;The traditional approach is based on top-level ontology, and the strategy is to utilize ontology that is comprehensive enough to cover a broad spectrum of domains through their universal terminologies. In this way, all domain ontologies share a common top-level formal ontology in which their respective root nodes can be defined, and hence consistency is assured across the knowledge graph. Nevertheless, the most recent approach is quite different and is a refinement of the eXtreme Ontology Design methodology based on the ontology design patterns. Whole ontology is viewed as a collection of interconnected modules, and modules are developed around the classified fundamental notions according to experts' terminology or the use-case. Having developed modules in a fashion of divide and conquer, these modules are shareable and reusable among some other ontology if needed, and consequently, the ontology being FAIR is justified (findable, accessible, interoperable, and reusable).&lt;/p&gt;

&lt;p&gt;Although, it has been argued that there are advantages to either paradigm, it is possible to have a combination of both approaches mentioned earlier, depending upon the use-case or the preferences of the ontology engineers. We provide an extension to the Protégé - based modular ontology engineering tool CoModIDE, in order to make it possible for ontology engineers to follow traditional, ad-hoc ontology modeling approach, alongside more modern paradigms such as modular ontology engineering. The project focuses on domain-level ontology developers or organizations dealing with ontology development, which may get help through the plugin in minimizing the tooling gap to unite paradigms and develop robust, flexible ontologies suitable to their needs. As a bridge between the more recently proposed modular ontology modeling approach and more classical ones based on foundational ontologies, it enables a best-of-both-worlds approach for ontology engineering.&amp;nbsp;&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Goonmeet Baja</style></author><author><style face="normal" font="default" size="100%">William Romine</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Amir Hassan Monadjemi</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">John M. Meddar</style></author><author><style face="normal" font="default" size="100%">Annie Myers</style></author><author><style face="normal" font="default" size="100%">Jyotishman Pathak</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multimodal mental health analysis in social media</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS ONE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Explainable Machine Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Hypothesis Testing</style></keyword><keyword><style  face="normal" font="default" size="100%">National Language Processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">Regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&amp;type=printable</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;Depression is a major public health concern in the U.S. and globally. While successful early&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;identification and treatment can lead to many positive health and behavioral outcomes,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;depression, remains undiagnosed, untreated or undertreated due to several reasons,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;including denial of the illness as well as cultural and social stigma. With the ubiquity of social&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;media platforms, millions of people are now sharing their online persona by expressing their&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;thoughts, moods, emotions, and even their daily struggles with mental health on social&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;media. Unlike traditional observational cohort studies conducted through questionnaires&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;and self-reported surveys, we explore the reliable detection of depressive symptoms from&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;data to discern depressive behaviors using a wide variety of features including individuallevel&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;demographics. By developing a multimodal framework and employing statistical techniques&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;to fuse heterogeneous sets of features obtained through the processing of visual,&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;textual, and user interaction data, we significantly enhance the current state-of-the-art&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;approaches for identifying depressed individuals on Twitter (improving the average F1-&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;Score by 5 percent) as well as facilitate demographic inferences from social media. Besides&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;providing insights into the relationship between demographics and mental health, our&lt;/p&gt;

&lt;p class=&quot;p1&quot;&gt;research assists in the design of a new breed of demographic-aware health interventions.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Federico Bianchi</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural-Symbolic Integration and the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.semantic-web-journal.net/content/neural-symbolic-integration-and-semantic-web-0</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Quinn Hirt</style></author><author><style face="normal" font="default" size="100%">Christopher Stevens</style></author><author><style face="normal" font="default" size="100%">Christopher W. Myers</style></author><author><style face="normal" font="default" size="100%">Benji Maruyama</style></author><author><style face="normal" font="default" size="100%">Colin Kupitz</style></author><author><style face="normal" font="default" size="100%">Dario Salvucci</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology of Instruction 1.0</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mina Abd Nikooie Pour</style></author><author><style face="normal" font="default" size="100%">Alsayed Algergawy</style></author><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Daniel Faria</style></author><author><style face="normal" font="default" size="100%">Irini Fundulaki</style></author><author><style face="normal" font="default" size="100%">Ian Harrow</style></author><author><style face="normal" font="default" size="100%">Sven Hertling</style></author><author><style face="normal" font="default" size="100%">Ernesto Jiménez-Ruiz</style></author><author><style face="normal" font="default" size="100%">Clement Jonquet</style></author><author><style face="normal" font="default" size="100%">Naouel Karam</style></author><author><style face="normal" font="default" size="100%">Abderrahmane Khiat</style></author><author><style face="normal" font="default" size="100%">Amir Laadhar</style></author><author><style face="normal" font="default" size="100%">Patrick Lambrix</style></author><author><style face="normal" font="default" size="100%">Huanyu Li</style></author><author><style face="normal" font="default" size="100%">Ying Li</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Heiko Paulheim</style></author><author><style face="normal" font="default" size="100%">Catia Pesquita</style></author><author><style face="normal" font="default" size="100%">Tzanina Saveta</style></author><author><style face="normal" font="default" size="100%">Pavel Shvaiko</style></author><author><style face="normal" font="default" size="100%">Andrea Splendiani</style></author><author><style face="normal" font="default" size="100%">Elodie Thieblin</style></author><author><style face="normal" font="default" size="100%">Cassia Trojahn</style></author><author><style face="normal" font="default" size="100%">Jana Vatascinova</style></author><author><style face="normal" font="default" size="100%">Beyza Yaman</style></author><author><style face="normal" font="default" size="100%">Ondrej Zamazal</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Results of theOntology Alignment Evaluation Initiative 2020</style></title><secondary-title><style face="normal" font="default" size="100%">15th International Workshop on Ontology Matching</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%"> A Review Of The Semantic Web Field</style></title><secondary-title><style face="normal" font="default" size="100%">Communications of the ACM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We review two decades of Semantic Web research and applications, discuss relationships to some other disciplines, and current challenges in the field.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefano Borgo</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Time is ripe to embrace the scientific approach in Applied Ontology</style></title><secondary-title><style face="normal" font="default" size="100%">Appl. Ontology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.3233/AO-200237</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">245–249</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Ryan McGranaghan</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Adam C. Kellerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Modular Ontology for Space Weather Research</style></title><secondary-title><style face="normal" font="default" size="100%">CoRR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2009.12285</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">abs/2009.12285</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards automated complex ontology alignment using rule-based machine learning</style></title><secondary-title><style face="normal" font="default" size="100%">Kansas State University</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><publisher><style face="normal" font="default" size="100%">Kansas State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Manhattan</style></pub-location><volume><style face="normal" font="default" size="100%">Doctorate</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Dissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Elodie Thieblin</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Daniel Faria</style></author><author><style face="normal" font="default" size="100%">Catia Pesquita</style></author><author><style face="normal" font="default" size="100%">Cassia Trojahn</style></author><author><style face="normal" font="default" size="100%">Ondrej Zamazal</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Evaluating Complex Ontology Alignments</style></title><secondary-title><style face="normal" font="default" size="100%">Knowledge Engineering Review</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">35</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">e21</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Joshua Schwartz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Srikanth Nadella</style></author><author><style face="normal" font="default" size="100%">Brandon Minnery</style></author><author><style face="normal" font="default" size="100%">Ion Juvina</style></author><author><style face="normal" font="default" size="100%">Michael L. Raymer</style></author><author><style face="normal" font="default" size="100%">William R. Aue</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Wikipedia Knowledge Graph for Explainable AI</style></title><secondary-title><style face="normal" font="default" size="100%">Second Iberoamerican Knowledge Graphs and Semantic Web Conference (KGSWC)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2020</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Explainable artificial intelligence (XAI) requires domain information to explain a system's decisions, for which structured forms of domain information like Knowledge Graphs (KGs) or ontologies are best suited. As such, readily available KGs are important to accelerate progress in XAI. To facilitate the advancement of XAI, we present the Wikipedia Knowledge Graph (WKG), based on information from English Wikipedia. Each Wikipedia article title, its corresponding category, and the category hierarchy are transformed into different entities in the knowledge graph. As the Wikipedia category hierarchy is not a tree, instead forming a graph, to make the finding process of the parent category easier, we break cycles in the category hierarchy. We evaluate whether the WKG is helpful to improve XAI compared with existing KGs, finding that WKG is better suited than the current state of the art. We also compare the cycle-free WKG with the Suggested Upper Merged Ontology (SUMO) and DBpedia schema KGs, finding minimal to no information loss.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Dalia Varanka</style></author><author><style face="normal" font="default" size="100%">Fatima Arauz</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Alignment of Surface Water Ontologies: A comparison of manual and automated approaches</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Geographical Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Alignment Rules from GBO to GMO</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AROA Results of 2019 OAEI</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Matching</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR</style></publisher><pub-location><style face="normal" font="default" size="100%">Auckland, New Zealand</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bianchi, Federico</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Capabilities of Logic Tensor Networks for Deductive Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">AAAI Spring Symposium 2019</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A closer look at the Semantic Web journal's review process</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.3233/SW-180342</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Karl Hammar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CoModIDE - The Comprehensive Modular Ontology IDE</style></title><secondary-title><style face="normal" font="default" size="100%">18th International Semantic Web Conference: Satellite Events</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Federico Bianchi</style></author><author><style face="normal" font="default" size="100%">Matteo Palmonari</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Luciano Serafini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Complementing Logical Reasoning with Sub-symbolic Commonsense</style></title><secondary-title><style face="normal" font="default" size="100%">Rules and Reasoning - Third International Joint Conference, RuleML+RR 2019, Bolzano, Italy, September 16-19, 2019</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garrett Goodman</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Iosif Papadakis Ktistakis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Constrained State-Preserved Extreme Learning Machine</style></title><secondary-title><style face="normal" font="default" size="100%">31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019, Portland, OR, USA, November 4-6, 2019</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1109/ICTAI.2019.00109</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">752–759</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Concept Induction for Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">AAAI Conference on Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2019</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">AAAI</style></publisher><pub-location><style face="normal" font="default" size="100%">Honolulu, US</style></pub-location><volume><style face="normal" font="default" size="100%">33</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Concept Induction refers to the problem of creating complex Description Logic class descriptions (i.e., TBox axioms) from instance examples (i.e.,&amp;nbsp; ABox data). In this paper we look particularly at the case where both a set of positive and a set of negative instances are given, and complex class expressions are sought under which the positive but not the negative examples fall. Concept induction has found applications in ontology engineering, but existing algorithms have fundamental performance issues in some scenarios, mainly because a high number of invokations of an external Description Logic reasoner is usually required. In this paper we present a new algorithm for this problem which drastically reduces the number of reasoner invokations needed. While this comes at the expense of a more limited traversal of the search space, we show that our approach improves execution times by up to several orders of magnitude, while output correctness, measured in the amount of correct coverage of the input instances, remains reasonably high in many cases. Our approach thus should provide a strong alternative to existing systems, in particular in settings where other systems are prohibitively slow.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Smeaton, Alan F.</style></author><author><style face="normal" font="default" size="100%">Graham, Yvette</style></author><author><style face="normal" font="default" size="100%">McGuinness, Kevin</style></author><author><style face="normal" font="default" size="100%">O'Connor, Noel E.</style></author><author><style face="normal" font="default" size="100%">Quinn, Seán</style></author><author><style face="normal" font="default" size="100%">Arazo Sanchez, Eric</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring the Impact of Training Data Bias on Automatic Generation of Video Captions</style></title><secondary-title><style face="normal" font="default" size="100%">MultiMedia Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">178–190</style></pages><isbn><style face="normal" font="default" size="100%">978-3-030-05710-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A major issue in machine learning is availability of training data. While this historically referred to the availability of a sufficient volume of training data, recently this has shifted to the availability of sufficient unbiased training data. In this paper we focus on the effect of training data bias on an emerging multimedia application, the automatic captioning of short video clips. We use subsets of the same training data to generate different models for video captioning using the same machine learning technique and we evaluate the performances of different training data subsets using a well-known video caption benchmark, TRECVid. We train using the MSR-VTT video-caption pairs and we prune this to reduce and make the set of captions describing a video more homogeneously similar, or more diverse, or we prune randomly. We then assess the effectiveness of caption-generating trained with these variations using automatic metrics as well as direct assessment by human assessors. Our findings are preliminary and show that randomly pruning captions from the training data yields the worst performance and that pruning to make the data more homogeneous, or diverse, does improve performance slightly when compared to random. Our work points to the need for more training data, both more video clips but, more importantly, more captions for those videos.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Quinn Hirt</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extensions to the Ontology Design Pattern Representation Language</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 10th Workshop on Ontology Design and Patterns (WOP 2019) co-located with 18th International Semantic Web Conference (ISWC 2019), Auckland, New Zealand, October 27, 2019</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-2459/short2.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">2459</style></volume><pages><style face="normal" font="default" size="100%">76–75</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Quinn Hirt</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Method for Automatically Generating Schema Diagrams for OWL Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">1st Iberoamerican Knowledge Graph and Semantic Web Conference (KGSWC)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">design patterns</style></keyword><keyword><style  face="normal" font="default" size="100%">evaluation</style></keyword><keyword><style  face="normal" font="default" size="100%">implementation</style></keyword><keyword><style  face="normal" font="default" size="100%">ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">schema diagrams</style></keyword><keyword><style  face="normal" font="default" size="100%">visualization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2019</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Villa Clara, Cuba</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Interest in Semantic Web technologies, including knowledge graphs and ontologies, is increasing rapidly in industry and academics. In order to support ontology engineers and domain experts, it is necessary to provide them with robust tools that facilitate the ontology engineering process. Often, the schema diagram of an ontology is the most important tool for quickly conveying the overall purpose of an ontology. In this paper, we present a method for programmatically generating a schema diagram from an OWL file. We evaluate its ability to generate schema diagrams similar to manually drawn schema diagrams and show that it outperforms VOWL and OWLGrEd. In addition, we provide a prototype implementation of this tool.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">149-161</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Quinn Hirt</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MODL: a Modular Ontology Design Library</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Ontology Design and Patterns</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Miriam Fernandez</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Amrapali Zaveri</style></author><author><style face="normal" font="default" size="100%">Alasdair Gray</style></author><author><style face="normal" font="default" size="100%">Vanessa Lopez</style></author><author><style face="normal" font="default" size="100%">Armin Haller</style></author><author><style face="normal" font="default" size="100%">Karl Hammar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Semantic Web. 16th International Conference, ESWC 2019, Portoroz, Slovenia, June 2-6, 2019, Proceedings</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><volume><style face="normal" font="default" size="100%">11503</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Sabrina Kirrane</style></author><author><style face="normal" font="default" size="100%">Olaf Hartig</style></author><author><style face="normal" font="default" size="100%">Victor de Boer</style></author><author><style face="normal" font="default" size="100%">Maria-Esther Vidal</style></author><author><style face="normal" font="default" size="100%">Maria Maleshkova</style></author><author><style face="normal" font="default" size="100%">Stefan Schlobach</style></author><author><style face="normal" font="default" size="100%">Karl Hammar</style></author><author><style face="normal" font="default" size="100%">Nelia Lasierra</style></author><author><style face="normal" font="default" size="100%">Steffen Stadtmüller</style></author><author><style face="normal" font="default" size="100%">Katja Hose</style></author><author><style face="normal" font="default" size="100%">Ruben Verborgh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Semantic Web: ESWC 2019 Satellite Events. Portoroz, Slovenia, June 2-6, 2019, Revised Selected Papers</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes In Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><volume><style face="normal" font="default" size="100%">11762</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Quinn, Seán</style></author><author><style face="normal" font="default" size="100%">Mileo, Alessandra</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Architecture-Agnostic Neural Transfer: a Knowledge-Enhanced Approach</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI-19}</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">7</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.24963/ijcai.2019/915</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">International Joint Conferences on Artificial Intelligence Organization</style></publisher><pages><style face="normal" font="default" size="100%">6452–6453</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Association Rule-Based Complex Ontology Alignment</style></title><secondary-title><style face="normal" font="default" size="100%">Joint International Semantic Technology Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Hangzhou, China</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Quinn, Seán</style></author><author><style face="normal" font="default" size="100%">Murphy, Noel</style></author><author><style face="normal" font="default" size="100%">Smeaton, Alan F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption</style></title><secondary-title><style face="normal" font="default" size="100%">2nd International Conference on Sensors, Signal and Image Processing (SSIP 19)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Ambient Assisted Living</style></keyword><keyword><style  face="normal" font="default" size="100%">Home Monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet of Things</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensor Applications</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensor Networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Prague, Czech Republic</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;People are living longer than ever due to advances in healthcare, and this has prompted many healthcare providers to look towards remote patient care as a means to meet the needs of the future. It is now a priority to enable people to reside in their own homes rather than in overburdened facilities whenever possible. The increasing maturity of IoT technologies and the falling costs of connected sensors has made the deployment of remote healthcare at scale an increasingly attractive prospect. In this work we demonstrate that we can measure the consistency and regularity of the behaviour of a household using sensor readings generated from interaction with the home environment. We show that we can track changes in this behaviour regularity longitudinally and detect changes that may be related to significant life events or trends that may be medically significant. We achieve this using periodicity analysis on water usage readings sampled from the main household water meter every 15 minutes for over 8 months. We utilise an IoT Application Enablement Platform in conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area Network in order to validate a data collection methodology that could be deployed at large scale in future. We envision the statistical methods described here being applied to data streams from the homes of elderly and at-risk groups, both as a means of&amp;nbsp; early illness&amp;nbsp; detection&amp;nbsp; and&amp;nbsp; for&amp;nbsp; monitoring&amp;nbsp; the well-being of those with known illnesses.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garrett Goodman</style></author><author><style face="normal" font="default" size="100%">Abby Edwards</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Tanvi Banerjee</style></author><author><style face="normal" font="default" size="100%">Jennifer Hughes</style></author><author><style face="normal" font="default" size="100%">William Romine</style></author><author><style face="normal" font="default" size="100%">Larry Lawhorne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Caregiver Assessment Using Smart Gaming Technology: A Preliminary Approach</style></title><secondary-title><style face="normal" font="default" size="100%">CoRR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1802.03051</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">abs/1802.03051</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Complex Alignment Benchmark: Geolink dataset</style></title><secondary-title><style face="normal" font="default" size="100%">ISWC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Elodie Thieblin</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Cassia Trojahn</style></author><author><style face="normal" font="default" size="100%">Ondrej Zamazal</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The First Version of the OAEI Complex Alignment Benchmark</style></title><secondary-title><style face="normal" font="default" size="100%">ISWC Poster and Demo Session</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefano Borgo</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Oliver Kutz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Formal Ontology in Information Systems - Proceedings of the 10th International Conference, FOIS 2018, Cape Town, South Africa, 19-21 September 2018</style></title><secondary-title><style face="normal" font="default" size="100%">FOIS 2018</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Tom Narock</style></author><author><style face="normal" font="default" size="100%">Matt Jones</style></author><author><style face="normal" font="default" size="100%">Peng Ji</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The GeoLink Knowledge Graph</style></title><secondary-title><style face="normal" font="default" size="100%">Big Earth Data</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Tom Narock</style></author><author><style face="normal" font="default" size="100%">Matt Jones</style></author><author><style face="normal" font="default" size="100%">Peng Ji</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The GeoLink Knowledge Graph</style></title><secondary-title><style face="normal" font="default" size="100%">Big Earth Data</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yolanda Gil</style></author><author><style face="normal" font="default" size="100%">Suzanne Pierce</style></author><author><style face="normal" font="default" size="100%">Hassan Babaie</style></author><author><style face="normal" font="default" size="100%">Arindam Banerjee</style></author><author><style face="normal" font="default" size="100%">Kirk Borne</style></author><author><style face="normal" font="default" size="100%">Gary Bust</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Imme Ebert-Uphoff</style></author><author><style face="normal" font="default" size="100%">Carla Gomes</style></author><author><style face="normal" font="default" size="100%">Mary Hill</style></author><author><style face="normal" font="default" size="100%">John Horel</style></author><author><style face="normal" font="default" size="100%">Leslie Hsu</style></author><author><style face="normal" font="default" size="100%">Jim Kinter</style></author><author><style face="normal" font="default" size="100%">Craig Knoblock</style></author><author><style face="normal" font="default" size="100%">David Krum</style></author><author><style face="normal" font="default" size="100%">Vipin Kumar</style></author><author><style face="normal" font="default" size="100%">Pierre Lermusiaux</style></author><author><style face="normal" font="default" size="100%">Yan Liu</style></author><author><style face="normal" font="default" size="100%">Chris North</style></author><author><style face="normal" font="default" size="100%">Victor Pankratius</style></author><author><style face="normal" font="default" size="100%">Shanan Peters</style></author><author><style face="normal" font="default" size="100%">Beth Plale</style></author><author><style face="normal" font="default" size="100%">Allen Pope</style></author><author><style face="normal" font="default" size="100%">Sai Ravela</style></author><author><style face="normal" font="default" size="100%">Juan Restrepo</style></author><author><style face="normal" font="default" size="100%">Aaron Ridley</style></author><author><style face="normal" font="default" size="100%">Hanan Samet</style></author><author><style face="normal" font="default" size="100%">Shashi Shekhar</style></author><author><style face="normal" font="default" size="100%">Katie Skinner</style></author><author><style face="normal" font="default" size="100%">Padhraic Smyth</style></author><author><style face="normal" font="default" size="100%">Basil Tikoff</style></author><author><style face="normal" font="default" size="100%">Lynn Yarmey</style></author><author><style face="normal" font="default" size="100%">Jia Zhang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intelligent Systems for Geosciences - An Essential Research Agenda</style></title><secondary-title><style face="normal" font="default" size="100%">Communications of the ACM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Journey From Simple to Complex Alignment on Real-World Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">ISWC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohammad Saeid Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Mohammadreza Rezvan</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Mohammadamin Barekatain</style></author><author><style face="normal" font="default" size="100%">Peyman Adibi</style></author><author><style face="normal" font="default" size="100%">Payam Barnaghi</style></author><author><style face="normal" font="default" size="100%">Amit P. Sheth</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine learning for internet of things data analysis: a survey</style></title><secondary-title><style face="normal" font="default" size="100%">Digital Communications and Networks</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Internet of Things</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Smart City</style></keyword><keyword><style  face="normal" font="default" size="100%">Smart data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S235286481730247X</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">161-175</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yazdavar, Amir Hossein</style></author><author><style face="normal" font="default" size="100%">Mahdavinejad, Mohammad Saied</style></author><author><style face="normal" font="default" size="100%">Bajaj, Goonmeet</style></author><author><style face="normal" font="default" size="100%">Thirunarayan, Krishnaprasad</style></author><author><style face="normal" font="default" size="100%">Pathak, Jyotishman</style></author><author><style face="normal" font="default" size="100%">Sheth, Amit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mental Health Analysis Via Social Media Data</style></title><secondary-title><style face="normal" font="default" size="100%">2018 IEEE International Conference on Healthcare Informatics (ICHI)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><pages><style face="normal" font="default" size="100%">459-460</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author><author><style face="normal" font="default" size="100%">Mohammad Saied Mahdavinejad</style></author><author><style face="normal" font="default" size="100%">Goonmeet Bajaj</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">Jyotishman Pathak</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mental Health Analysis Via Social Media Data, IEEE ICHI 2018</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE, ICHI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modular Ontologies as a Bridge Between Human Conceptualization and Data</style></title><secondary-title><style face="normal" font="default" size="100%">Graph-Based Representation and Reasoning - 23rd International Conference on Conceptual Structures, ICCS 2018, Edinburgh, UK, June 20-22, 2018, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-319-91379-7\_1</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">10872</style></volume><pages><style face="normal" font="default" size="100%">3–6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Clare Paul</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Design Patterns for Winston's Taxonomy Of Part-Whole Relations</style></title><secondary-title><style face="normal" font="default" size="100%">Emerging Topics in Semantic Technologies - ISWC 2018 Satellite Events [best papers from 13 of the workshops co-located with the ISWC 2018 conference]</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.3233/978-1-61499-894-5-119</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">119–129</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Catia Pesquita</style></author><author><style face="normal" font="default" size="100%">Daniela Oliveira</style></author><author><style face="normal" font="default" size="100%">Helena B. McCurdy</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The Properties of Property Alignment on the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Metadata, Semantics and Ontologies</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">13</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">42</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Blake Regalia</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Gengchen Mai</style></author><author><style face="normal" font="default" size="100%">Stephanie Delbecque</style></author><author><style face="normal" font="default" size="100%">Maarten Fröhlich</style></author><author><style face="normal" font="default" size="100%">Patrick Mertinent</style></author><author><style face="normal" font="default" size="100%">Trevor Lazarus</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Prospects of Blockchain and Distributed Ledger Technologies for Open Science and Academic Publishing</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.semantic-web-journal.net/content/prospects-blockchain-and-distributed-ledger-technologies-open-science-and-academic</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Quinn Hirt</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Protégé Plug-In for Annotating OWL Ontologies with OPLa</style></title><secondary-title><style face="normal" font="default" size="100%">ESWC (Satellite Events)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">11155</style></volume><pages><style face="normal" font="default" size="100%">23–27</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pseudo-Random ALC Syntax Generation</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web: ESWC 2018 Satellite Events - ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ALC</style></keyword><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">DL</style></keyword><keyword><style  face="normal" font="default" size="100%">random generation</style></keyword><keyword><style  face="normal" font="default" size="100%">synthetic data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-319-98192-5\_4</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heraklion, Crete, Greece</style></pub-location><volume><style face="normal" font="default" size="100%">11155</style></volume><pages><style face="normal" font="default" size="100%">19–22</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-98191-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We discuss a tool capable of rapidly generating pseudo-random syntactically valid ALC expression trees. The program is meant to allow a researcher to create large sets of independently valid expressions with a minimum of personal bias for experimentation.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ebrahimi, Monireh</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Bianchi, Federico</style></author><author><style face="normal" font="default" size="100%">Xie, Ning</style></author><author><style face="normal" font="default" size="100%">Doran, Derek</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reasoning over RDF Knowledge Bases using Deep Learning</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:1811.04132</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1811.04132</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular the scalability issues arising from the ever-increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefano Borgo</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Some Open Issues After Twenty Years of Formal Ontology</style></title><secondary-title><style face="normal" font="default" size="100%">FOIS 2018</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ebooks.iospress.nl/publication/50236</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Comprehensive Modular Ontology IDE and Tool Suite</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Doctoral Consortium at ISWC 2018 co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, October 8th - to - 12th, 2018.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-2181/paper-08.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">2181</style></volume><pages><style face="normal" font="default" size="100%">65–72</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Leah McEwen</style></author><author><style face="normal" font="default" size="100%">Quinn Hirt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Pattern-Based Ontology for Chemical Laboratory Procedures</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 9th Workshop on Ontology Design and Patterns (WOP 2018) co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, October 9th, 2018.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-2195/research_paper_1.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">2195</style></volume><pages><style face="normal" font="default" size="100%">40–51</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>34</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila A. Krisnadhi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Tutorial on Modular Ontology Modeling with Ontology Design Patterns: The Cooking Recipes Ontology</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karl Hammar</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Agnieszka Lawrynowicz</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Andrea Nuzzolese</style></author><author><style face="normal" font="default" size="100%">Monika Solanki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Advances in Ontology Design and Patterns</style></title><secondary-title><style face="normal" font="default" size="100%">Studies on the Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><volume><style face="normal" font="default" size="100%">32</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Challenges of Sentiment Analysis for Dynamic Events</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: sans-serif; font-size: 15px;&quot;&gt;Efforts to assess people's sentiments on Twitter have suggested that Twitter could be a valuable resource for studying political sentiment and that it reflects the offline political landscape. Many opinion mining systems and tools provide users with people's attitudes toward products, people, or topics and their attributes/aspects. However, although it may appear simple, using sentiment analysis to predict election results is difficult, since it is empirically challenging to train a successful model to conduct sentiment analysis on tweet streams for a dynamic event such as an election. This article highlights some of the challenges related to sentiment analysis encountered during monitoring of the presidential election using Kno.e.sis's Twitris system.&lt;/span&gt;&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ebrahimi, Monireh</style></author><author><style face="normal" font="default" size="100%">Yazdavar, Amir Hossein</style></author><author><style face="normal" font="default" size="100%">Sheth, Amit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Challenges of Sentiment Analysis for Dynamic Events</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Intelligent Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational Environment: An ODP to Support Finding and Recreating Computational Analyses</style></title><secondary-title><style face="normal" font="default" size="100%">8th Workshop on Ontology Design and Patterns - WOP2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila A. Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Core Pattern for Events</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Ontology Design and Patterns</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Reasoning Algorithms for Fragments of Horn Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Science and Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Reasoning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://rave.ohiolink.edu/etdc/view?acc_num=wright1491317096530938</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Wright State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Dayton</style></pub-location><volume><style face="normal" font="default" size="100%">Doctor of Philosophy (PhD)</style></volume><pages><style face="normal" font="default" size="100%">70</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We characterize two fragments of Horn Description Logics and we define two specialized reasoning algorithms that effectively solve the standard reasoning tasks over each of such fragments. We believe our work to be of general interest since (1) a rather large proportion of real-world Horn ontologies belong to some of these two fragments and (2) the implementations based on our reasoning approach significantly outperform state-of-the-art reasoners. Claims (1) and (2) are extensively proven via empirically evaluation.
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Ning Xie</style></author><author><style face="normal" font="default" size="100%">Derek Doran</style></author><author><style face="normal" font="default" size="100%">Michael Raymer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Explaining Trained Neural Networks with Semantic Web Technologies: First Steps</style></title><secondary-title><style face="normal" font="default" size="100%">Twelveth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://daselab.cs.wright.edu/nesy/NeSy17/</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">12</style></edition><pub-location><style face="normal" font="default" size="100%">London, UK</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Hilmar Lapp</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Ontological Modeling of Trees</style></title><secondary-title><style face="normal" font="default" size="100%">8th Workshop on Ontology Design and Patterns - WOP2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Holly Ferguson</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Aimee Buccellato</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern and Its Use Case for Modeling Material Transformation</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">731</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue><section><style face="normal" font="default" size="100%">719</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Microblog Entries</style></title><secondary-title><style face="normal" font="default" size="100%">8th Workshop on Ontology Design and Patterns (WOP2017)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Due to the exponential growth of the Internet of Things and use of Social Media Platforms, observers have an unprecedented level of detailed information available on the behavior of communities. However, due to the highly heterogeneous nature and the immense volume of the data, a composite view is difficult to generate. Such a composite view would be exceptionally useful in the realms of insider threat detection, after-action forensics, and hazardous situation detection and avoidance. The Semantic Web, via ontology modeling, offers a powerful tool for fusing the disparate data sources and formats. To this end, we have created an ontology design pattern (ODP) for the modeling of a simple microblog entry. This ODP is intended to fit within an ecosystem for fusing social media, support advanced visualization, and provide a preliminary framework for trust assessment.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maryam Labaf</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Anthony B. Evans</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Propositional Rule Extraction from Neural Networks under Background Knowledge</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematics and Statistical Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2017</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">Master</style></volume><pages><style face="normal" font="default" size="100%">50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;It is well-known that the input-output behaviour of a neural network can be recast in terms of a set of propositional rules, and under certain weak preconditions this is also always possible with positive (or definite) rules. Furthermore, in this case there is in fact a unique minimal (technically, reduced) set of such rules which perfectly captures the inputoutput mapping. In this paper, we investigate to what extent these results and corresponding rule extraction algorithms can be lifted to take additional background knowledge into account. It turns out that uniqueness of the solution can then no longer be guaranteed. However, the background knowledge often makes it possible to extract simpler, and thus more easily understandable, rulesets which still perfectly capture the input-output mapping.&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Master thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maryam Labaf</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></secondary-authors><tertiary-authors><author><style face="normal" font="default" size="100%">Anthony B. Evans</style></author></tertiary-authors></contributors><titles><title><style face="normal" font="default" size="100%"> Propositional rule extraction from neural networks under background knowledge</style></title><secondary-title><style face="normal" font="default" size="100%">Twelfth International Workshop on Neural-Symbolic Learning and Reasoning</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Background knowledge</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Network</style></keyword><keyword><style  face="normal" font="default" size="100%">Propositional Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Rule Extraction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2017</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kalpa Gunaratna</style></author><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Gong Cheng</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Relatedness-based Multi-Entity Summarization</style></title><secondary-title><style face="normal" font="default" size="100%">IJCAI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-ofthe-art entity summarization approaches.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ning Xie</style></author><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Derek Doran</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Michael Raymer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Relating Input Concepts to Convolutional Neural Network Decisions</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS 2017 Workshop: Interpreting, Explaining and Visualizing Deep Learning, NIPS IEVDL 2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2017</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">NIPS</style></publisher><pub-location><style face="normal" font="default" size="100%">CA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Many current methods to interpret convolutional neural networks (CNNs) use visualization techniques and words to highlight concepts of the input seemingly relevant to a CNN’s decision. The methods hypothesize that the recognition of these concepts are instrumental in the decision a CNN reaches, but the nature of this relationship has not been well explored. To address this gap, this paper examines the quality of a concept’s recognition by a CNN and the degree to which the recognitions are associated with CNN decisions. The study considers a CNN trained for scene recognition over the ADE20k dataset. It uses a novel approach to find and score the strength of minimally distributed representations of input concepts (defined by objects in scene images) across late stage feature maps. Subsequent analysis finds evidence that concept recognition impacts decision making. Strong recognition of concepts frequently-occurring in few scenes are indicative of correct decisions, but recognizing concepts common to many scenes may mislead the network.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Matthew Horridge</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Rendering OWL in Description Logic Syntax</style></title><secondary-title><style face="normal" font="default" size="100%">ESWC 2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Rendering OWL in LaTeX for Improved Readability: Extensions to the OWLAPI</style></title><secondary-title><style face="normal" font="default" size="100%">Department of Computer Science and Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2017</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Wright State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Dayton, Ohio</style></pub-location><volume><style face="normal" font="default" size="100%">Master of Science</style></volume><pages><style face="normal" font="default" size="100%">106</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;As ontology engineering is inherently a multidisciplinary process, it is necessary to utilize multiple vehicles to present an ontology to a user. In order to examine the content of an ontology, formal logic renderings of the axioms appear to be a very helpful approach for some. This thesis introduces a number of incremental improvements to the OWLAPI's \LaTeX{} rendering framework in order to improve the readability, concision, and correctness of OWL files translated into Description Logic and First Order Logic. In addition, we examine the efficacy of these renderings as vehicles for understanding an ontology.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Replication Study: Understanding What Drives the Performance in WikiMatch</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://disi.unitn.it/~pavel/om2017/papers/om2017_poster5.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We replicate and demonstrate that the performance of the WikiMatch automated ontology alignment system may be driven not by the particular information from Wikipedia directly used by the system, but rather by string similarity and Wikipedia’s manually curated synonym sets, as encoded in the site’s query resolution and page redirection system. In order to gain a detailed understanding of how Wikipedia contributes to WikiMatch, we replicate results reported for WikiMatch and analyze the results to evaluate our hypothesis.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Rule-based OWL Modeling with ROWLTab Protege Plugin</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;It has been argued that it is much easier to convey logi- cal statements using rules rather than OWL (or description logic (DL)) axioms. Based on recent theoretical developments on transformations between rules and DLs, we have developed ROWLTab, a Prot ́eg ́e plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL 2 DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule. In this paper, we present ROWLTab, together with a user evaluation of its effectiveness compared to entering axioms using the standard Prot ́eg ́e interface. Our evaluation shows that modeling with ROWLTab is much quicker than the standard interface, while at the same time, also less prone to errors for hard modeling tasks.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yazdavar, Amir Hossein</style></author><author><style face="normal" font="default" size="100%">Al-Olimat, Hussein S</style></author><author><style face="normal" font="default" size="100%">Ebrahimi, Monireh</style></author><author><style face="normal" font="default" size="100%">Bajaj, Goonmeet</style></author><author><style face="normal" font="default" size="100%">Banerjee, Tanvi</style></author><author><style face="normal" font="default" size="100%">Thirunarayan, Krishnaprasad</style></author><author><style face="normal" font="default" size="100%">Pathak, Jyotishman</style></author><author><style face="normal" font="default" size="100%">Sheth, Amit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media</style></title><secondary-title><style face="normal" font="default" size="100%">ASONAM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amir Hossein Yazdavar</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hussein S. Al-Olimat</style></author><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Goonmeet Bajaj</style></author><author><style face="normal" font="default" size="100%">Tanvi Banerjee</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">Jyotishman Pathak</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media</style></title><secondary-title><style face="normal" font="default" size="100%">ASONAM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://dl.acm.org/doi/abs/10.1145/3110025.3123028</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: Merriweather, serif; font-size: 17px; background-color: rgb(250, 250, 250);&quot;&gt;With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.&lt;/span&gt;&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila A. Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Spatiotemporal Extent Pattern based on Semantic Trajectories</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Ontology Design and Patterns</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila A. Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Stub Metapattern</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Ontology Design and Patterns</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Aldo Gangemi</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Adila A. Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Valentina Presutti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a simple but useful ontology design pattern representation language</style></title><secondary-title><style face="normal" font="default" size="100%">8th Workshop on Ontology Design and Patterns - WOP2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Freddy Lécué</style></author><author><style face="normal" font="default" size="100%">Jeff Z. Pan</style></author><author><style face="normal" font="default" size="100%">Jiewen Wu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AI for Traffic Analytics</style></title><secondary-title><style face="normal" font="default" size="100%">The IEEE Intelligent Informatics Bulletin</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">17</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">21</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karl Hammar</style></author><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Marieke van Erp</style></author><author><style face="normal" font="default" size="100%">Antske Fokkens</style></author><author><style face="normal" font="default" size="100%">Aldo Gangemi</style></author><author><style face="normal" font="default" size="100%">Willem Robert van Hage</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Tom Narock</style></author><author><style face="normal" font="default" size="100%">Roxane Segers</style></author><author><style face="normal" font="default" size="100%">Monika Solanki</style></author><author><style face="normal" font="default" size="100%">Vojtech Svatek</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Collected Research Questions Concerning Ontology Design Patterns</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Engineering with Ontology Design Patterns: Foundations and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Monika Solanki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Considerations regarding Ontology Design Patterns</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">1-7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gordon Watts</style></author><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Detector Final State pattern: Using the Web Ontology Language to describe a Physics Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Presented at the 17th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Valparaiso, Chile, January 2016.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Data and Software Preservation for Open Science (DASPOS) collaboration has developed an ontology for describing particle physics analyses. The ontology, a series of data triples, is designed to describe dataset, selection cuts, and measured quantities for an analysis. The ontology specification, written in the Web Ontology Language (OWL), is designed to be interpreted by many pre-existing tools, including search engines, and to apply to both theory and experiment published papers. This paper gives an introduction to OWL and this branch of library science from a particle physicist’s point of view, specifics of the Detector Final State Pattern, and how it is designed to be used in the field of particle physics primarily to archive and recall analyses. A general introduction to DASPOS and how its other work fits in with this topic will also be described.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Presented at the&amp;nbsp;&lt;span&gt;17&lt;sup&gt;th&lt;/sup&gt; International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Valparaiso, Chile, January 2016.&lt;/span&gt;&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Reasoning Algorithms for Fragments of Horn Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Department of Computer Science and Engineering, Wright State University</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Wright State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Dayton, OH, USA</style></pub-location><volume><style face="normal" font="default" size="100%">PhD</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We characterize two fragments of Horn Description Logics and we define two specialized reasoning algorithms that effectively solve the standard reasoning tasks over each of such fragments. We believe our work to be of general interest since (1) a rather large proportion of real-world Horn ontologies belong to some of these two fragments and (2) the implementations based on our reasoning approach significantly outperform state-of-the-art reasoners. Claims (1) and (2) are extensively proven via empirically evaluation.&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Dissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>9</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Final State Detector ODP OWL Ontology</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>9</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Final State Detector ODP RDF Data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yazdavar, Amir Hossein</style></author><author><style face="normal" font="default" size="100%">Ebrahimi, Monireh</style></author><author><style face="normal" font="default" size="100%">Salim, Naomie</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Soft Computing and Decision Support Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">7–18</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geospatial Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">The International Encyclopedia of Geography: People, the Earth, Environment, and Technology. Wiley/AAG</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geospatial Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">The International Encyclopedia of Geography: People, the Earth, Environment, and Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Wiley/AAG</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Karl Hammar</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">How to Document Ontology Design Patterns</style></title><secondary-title><style face="normal" font="default" size="100%">7th Workshop on Ontology and Semantic Web Patterns (WOP2016)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Kobe, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Aldo Gangemi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Valentina Presutti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Introduction: Ontology Design Patterns in a Nutshell</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Engineering with Ontology Design Patterns: Foundations and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aidan Hogan</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linked Dataset Description Papers at the Semantic Web Journal: A 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Thomas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linked Ocean Data 2.0</style></title><secondary-title><style face="normal" font="default" size="100%">Oceanographic and Marine Cross-Domain Data Management for Sustainable Development</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><pages><style face="normal" font="default" size="100%">69-99</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joshi, Amit Krishna</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Dong, Guozhu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">LinkGen: Multipurpose linked data generator</style></title><secondary-title><style face="normal" font="default" size="100%">International Semantic Web Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">113–121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Chandan Patel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Matching Instances in GeoLink</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Matching Workshop -- ISWC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modeling OWL with Rules: The ROWL Protege Plugin</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1690/paper92.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">15th International Semantic Web Conference (ISWC) 2016</style></publisher><pub-location><style face="normal" font="default" size="100%">Kobe, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Abstract. In our experience, some ontology users find it much easier to convey logical statements using rules rather than OWL (or description logic) axioms. Based on recent theoretical developments on transformations between rules and description logics, we develop ROWL, a Proteg´ e plugin that allows users to enter OWL axioms by way of rules; the plugin then automatically converts these rules into OWL DL axioms if possible, and prompts the user in case such a conversion is not possible without weakening the semantics of the rule.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modeling With Ontology Design Patterns: Chess Games As a Worked Example</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Engineering with Ontology Design Patterns: Foundations and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><number><style face="normal" font="default" size="100%">Studies on the Semantic Web</style></number><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">3–21</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">1</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Ferguson, Holly</style></author><author><style face="normal" font="default" size="100%">Charles, Vardeman</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Modification to the Hazardous Situation ODP to Support Risk Assessment and Mitigation</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Ontology Design Patterns (WOP)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">hazard</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Design Pattern</style></keyword><keyword><style  face="normal" font="default" size="100%">risk assessment</style></keyword><keyword><style  face="normal" font="default" size="100%">risk mitigation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2016</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Hazardous Situation ontology design pattern models the consequences of exposure of an object to a hazard. In its current form, the ODP is well suited for representing the consequences of exposure after the fact, which is very useful for applications such as damage assessment and recovery planning. In this work, we present a modification to this pattern that enables it to additionally support proactive questions central to risk assessment and mitigation planning.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>45</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Ferguson, Holly</style></author><author><style face="normal" font="default" size="100%">Vardeman, Charles</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modified Hazardous Situation ODP</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Hazardous Situation ontology design pattern models the consequences of exposure of an object to a hazard. In its current form, the ODP is well suited for representing the consequences of exposure after the fact, which is very useful for applications such as damage assessment and recovery planning. In this work, we present a modification to this pattern that enables it to additionally support proactive questions central to risk assessment and mitigation planning.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modular Ontology Architecture for Data Integration in the GeoLink Project</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ontologforum.org/index.php?title=ConferenceCall_2016_02_25&amp;oldid=22543#hid1C2C</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Ontology Summit 2016 (online)</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Design Patterns for Data Integration: The GeoLink Experience</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Engineering with Ontology Design Patterns: Foundations and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><number><style face="normal" font="default" size="100%">Studies on the Semantic Web</style></number><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">267 - 278</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">13</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Víctor Rodríguez-Doncel</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Design Patterns for Linked Data Publishing</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Engineering with Ontology Design Patterns: Foundations and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><number><style face="normal" font="default" size="100%">Studies on the Semantic Web</style></number><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">201 - 232</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">10</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Aldo Gangemi</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Valentina Presutti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Engineering with Ontology Design Patterns: Foundations and Applications</style></title><secondary-title><style face="normal" font="default" size="100%">Studies On the Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><volume><style face="normal" font="default" size="100%">025</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">OWLAx: A Protege Plugin to Support Ontology Axiomatization through Diagramming</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1690/paper83.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">15th International Semantic Web Conference, ISWC2016, Kobe, Japan, October 2016</style></publisher><pub-location><style face="normal" font="default" size="100%">Kobe, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Abstract. Once the conceptual overview, in terms of a somewhat informal class diagram, has been designed in the course of engineering an ontology, the process of adding many of the appropriate logical axioms is mostly a routine task. We provide a Prot´eg´e3 plugin which supports this task, together with a visual user interface, based on established methods for ontology design pattern modeling.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Cristina Feier</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Practical Acyclicity Notion for Query Answering Over Horn-SRIQ Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web - {ISWC} 2016 - 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part {I}</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-46523-4_5</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">70–85</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Conjunctive query answering over expressive Horn Description Logic ontologies is a relevant and challenging problem which, in some cases, can be addressed by application of the chase algorithm. In this paper, we define a novel acyclicity notion which provides a sufficient condition for termination of the restricted chase over Horn-SRIQ TBoxes. We show that this notion generalizes most of the existing acyclicity conditions (both theoretically and empirically). Furthermore, this new acyclicity notion gives rise to a very efficient reasoning procedure. We provide evidence for this by providing a materialization based reasoner for acyclic ontologies which outperforms other state-of-the-art systems.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Role Patterns</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Engineering with Ontology Design Patterns: Foundations and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><number><style face="normal" font="default" size="100%">Studies on the Semantic Web</style></number><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">313–319</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">16</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhangquan Zhou</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author><author><style face="normal" font="default" size="100%">Chang Liu</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reasoning with Large Scale OWL 2 EL Ontologies Based on MapReduce</style></title><secondary-title><style face="normal" font="default" size="100%">Web Technologies and Applications - 18th Asia-Pacific Web Conference, APWeb 2016, Suzhou, China, September 23-25, 2016</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monireh Ebrahimi</style></author><author><style face="normal" font="default" size="100%">Amir HosseinYazdavar</style></author><author><style face="normal" font="default" size="100%">Naomie Salim</style></author><author><style face="normal" font="default" size="100%">Safaa Eltyeb</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recognition of side effects as implicit-opinion words in drug reviews</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span&gt;Many opinion-mining systems and tools have been developed to provide users with the attitudes of people toward entities and their attributes or the overall polarities of documents. In addition, side effects are one of the critical measures used to evaluate a patient’s opinion for a particular drug. However, side effect recognition is a challenging task, since side effects coincide with disease symptoms lexically and syntactically. The purpose of this paper is to extract drug side effects from drug reviews as an integral implicit-opinion words.&lt;/span&gt;&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ebrahimi, Monireh</style></author><author><style face="normal" font="default" size="100%">Yazdavar, Amir Hossein</style></author><author><style face="normal" font="default" size="100%">Salim, Naomie</style></author><author><style face="normal" font="default" size="100%">Eltyeb, Safaa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recognition of side effects as implicit-opinion words in drug reviews</style></title><secondary-title><style face="normal" font="default" size="100%">Online Information Review</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">1018–1032</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohamad, E Tonnizam</style></author><author><style face="normal" font="default" size="100%">Armaghani, D Jahed</style></author><author><style face="normal" font="default" size="100%">Momeni, E</style></author><author><style face="normal" font="default" size="100%">Yazdavar, AH</style></author><author><style face="normal" font="default" size="100%">Ebrahimi, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Rock strength estimation: a PSO-based BP approach</style></title><secondary-title><style face="normal" font="default" size="100%">Neural Computing and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><pages><style face="normal" font="default" size="100%">1–12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Roles of Logical Axiomatizations for Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">Ontology Engineering with Ontology Design Patterns: Foundations and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Amsterdam</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pesquita, Catia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Data Integration</style></title><secondary-title><style face="normal" font="default" size="100%">Springer Handbook on Big Data</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jacob Miracle</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Web Enabled Record Linkage Attacks on Anonymized Data</style></title><secondary-title><style face="normal" font="default" size="100%">PrivOn Workshop at ISWC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pawel Grzebala</style></author><author><style face="normal" font="default" size="100%">Helena B. McCurdy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Best Practices for Crowdsourcing Ontology Alignment Benchmarks</style></title><secondary-title><style face="normal" font="default" size="100%">15th International Semantic Web Conference (ISWC) 2016</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><pub-location><style face="normal" font="default" size="100%">Kobe, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Ontology alignment systems establish the links between ontologies that enable knowledge from various sources and domains to be used by applications in many different ways. Unfortunately, these systems are not perfect. Currently, the results of even the best-performing alignment systems need to be manually verified in order to be fully trusted. Ontology alignment researchers have turned to crowdsourcing platforms such as Amazon's Mechanical Turk to accomplish this. However, there has been little systematic analysis of the accuracy of crowdsourcing for alignment verification and the establishment of best practices. In this work, we analyze the impact of the presentation of the context of potential matches and the way in which the question is presented to workers on the accuracy of crowdsourcing for alignment verification.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Wenbo Wang</style></author><author><style face="normal" font="default" size="100%">Keke Chen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on World Wide Web </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2016</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland</style></publisher><isbn><style face="normal" font="default" size="100%">978-1-4503-4143-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;ytab x-tabs-item-body&quot; id=&quot;abstract&quot;&gt;
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&lt;p&gt;&amp;nbsp;&lt;/p&gt;
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</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Tom Narock</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Update on ESIP Testbed Project</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>45</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Holly Ferguson</style></author><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use case for the Modified Hazardous Situation ODP</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joshi, Amit Krishna</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Dong, Guozhu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Alignment Aware Linked Data Compression</style></title><secondary-title><style face="normal" font="default" size="100%">Joint International Semantic Technology Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The success of linked data has resulted in a large amount of data being generated in a standard RDF format. Various techniques have been explored to generate a compressed version of RDF datasets for archival and transmission purpose. However, these compression techniques are designed to compress a given dataset without using any external knowledge, either through a compact representation or removal of semantic redundancies present in the dataset. In this paper, we introduce a novel approach to compress RDF datasets by exploiting alignments present across various datasets at both instance and schema level. Our system generates lossy compression based on the confidence value of relation between the terms. We also present a comprehensive evaluation of the approach by using reference alignment from OAEI.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pavan Kapanipathi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Are We Really Standing on the Shoulders of Giants?</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 1st Workshop on Negative or Inconclusive Results in Semantic Web (NoISE 2015) co-located with 12th Extended Semantic Web Conference (ESWC 2015) </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pub-location><style face="normal" font="default" size="100%">Portoroz, Slovenia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cristina Feier</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Giorgio Stefanoni</style></author><author><style face="normal" font="default" size="100%">Cuenca Grau, Bernardo</style></author><author><style face="normal" font="default" size="100%">Ian Horrocks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Combined Approach to Query Answering Beyond the OWL 2 Profiles</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cs.ox.ac.uk/isg/people/cristina.feier/ijcai_rsafinal.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Combined approaches have become a successful technique for CQ answering over ontologies. Existing algorithms, however, are restricted to the logics underpinning the OWL 2 profiles. Our goal is to make combined approaches applicable to a wider range of ontologies. We focus on RSA: a class of Horn ontologies that extends the profiles while ensuring tractability of standard reasoning. We show that CQ answering over RSA ontologies without role composition is feasible in NP. Our reasoning procedure generalises the combined approach for ELHO and DL-LiteR using an encoding of CQ answering into fact entailment w.r.t. a logic program with function symbols and stratified negation. Our results have significant practical implications since many out-of-profile Horn ontologies are RSA.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Perturbation via Randomized Normalization for Privacy Protection</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Prabhaker Mateti</style></author><author><style face="normal" font="default" size="100%">Freddy Lécué</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Distributed and Scalable OWL EL Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 12th Extended Semantic Web Conference (ESWC 2015) </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">DistEL</style></keyword><keyword><style  face="normal" font="default" size="100%">Distributed Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL EL</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Portoroz, Slovenia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;OWL 2 EL is one of the tractable proles of the Web Ontology&amp;nbsp;Language (OWL) which is a W3C-recommended standard. OWL 2&amp;nbsp;EL provides sucient expressivity to model large biomedical ontologies&amp;nbsp;as well as streaming data such as trac, while at the same time allows&amp;nbsp;for ecient reasoning services. Existing reasoners for OWL 2 EL, however,&amp;nbsp;use only a single machine and are thus constrained by memory and&amp;nbsp;computational power. At the same time, the automated generation of&amp;nbsp;ontological information from streaming data and text can lead to very&amp;nbsp;large ontologies which can exceed the capacities of these reasoners. We&amp;nbsp;thus describe a distributed reasoning system that scales well using a cluster&amp;nbsp;of commodity machines. We also apply our system to a use case on&amp;nbsp;city trac data and show that it can handle volumes which cannot be&amp;nbsp;handled by current single machine reasoners.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Distributed Reasoning over Ontology Streams and Large Knowledge Base</style></title><secondary-title><style face="normal" font="default" size="100%">NSF Data Science Workshop 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pub-location><style face="normal" font="default" size="100%">Seattle, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Robert A. Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Douglas Fils</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Peng Ji</style></author><author><style face="normal" font="default" size="100%">Matthew Jones</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Kerstin Lehnert</style></author><author><style face="normal" font="default" size="100%">Audrey Mickle</style></author><author><style face="normal" font="default" size="100%">Tom Narock</style></author><author><style face="normal" font="default" size="100%">Margaret O'Brien</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EarthCube GeoLink: Semantics and Linked Data for the Geosciences</style></title><secondary-title><style face="normal" font="default" size="100%">2015 American Geophysical Union Fall Meeting, San Francisco, 14-18 December 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cristina Feier</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Giorgio Stefanoni</style></author><author><style face="normal" font="default" size="100%">Cuenca Grau, Bernardo</style></author><author><style face="normal" font="default" size="100%">Ian Horrocks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extending the Combined Approach Beyond Lightweight Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 28th International Workshop on Description Logics (DL)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cs.ox.ac.uk/isg/people/cristina.feier/pdfs/dlmain.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Combined approaches have become a successful technique for CQ answering over ontologies. Existing algorithms, however, are restricted to the logics underpinning the OWL 2 profiles. Our goal is to make combined approaches applicable to a wider range of ontologies. We focus on RSA: a class of Horn ontologies that extends the profiles while ensuring tractability of standard reasoning. We show that CQ answering over RSA ontologies without role composition is feasible in NP. Our reasoning procedure generalises the combined approach for ELHO and DL-LiteR using an encoding of CQ answering into fact entailment w.r.t. a logic program with function symbols and stratified negation. Our results are significant in practice since many out-of-profile Horn ontologies are RSA.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">Krzsyztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Douglas Fils</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author><author><style face="normal" font="default" size="100%">Peng Ji</style></author><author><style face="normal" font="default" size="100%">Matthew Jones</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Kerstin Lehnert</style></author><author><style face="normal" font="default" size="100%">Audrey Mickle</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Margaret O'Brien</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The GeoLink Framework for Pattern-based Linked Data Integration</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ISWC 2015 Posters &amp; Demonstrations Track</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Douglas Fils</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author><author><style face="normal" font="default" size="100%">Peng Ji</style></author><author><style face="normal" font="default" size="100%">Matthew Jones</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Kerstin Lehnert</style></author><author><style face="normal" font="default" size="100%">Audrey Mickle</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Margaret O'Brien</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The GeoLink Modular Oceanography Ontology</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web - ISWC 2015. 14th International Semantic Web Conference, Bethlehem, Pennsylvania, United States, October 11-15, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Wenbo Wang</style></author><author><style face="normal" font="default" size="100%">Keke Chen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identifying Regrettable Messages from Tweets</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">International World Wide Web Conferences Steering Committee</style></publisher><isbn><style face="normal" font="default" size="100%">978-1-4503-3473-0</style></isbn><language><style face="normal" font="default" size="100%">English</style></language><work-type><style face="normal" font="default" size="100%">Posters</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Language for Inconsistency-Tolerant Ontology Mapping</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Science and Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">Doctor of Philosophy</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Ontology alignment plays a key role in enabling interoperability among various data sources present in the web. The nature of the world is such, that the same concepts differ in meaning, often so slightly, which makes it difficult to relate these concepts. It is the omni-present heterogeneity that is at the core of the web. The research work presented in this dissertation, is driven by the goal of providing a robust ontology alignment language for the semantic web, as we show that description logics based alignment languages are not suitable for aligning ontologies. The adoption of the semantic web technologies has been consistently on the rise over the past decade, and it continues to show promise. The core component of the semantic web is the set of knowledge representation languages -- mainly the W3C (World Wide Web Consortium) standards Web Ontology Language (OWL), Resource Description Framework (RDF), and Rule Interchange Format (RIF). While these languages have been designed in order to be suitable for the openness and extensibility of the web, they lack certain features which we try to address in this dissertation. One such missing component is the lack of non-monotonic features, in the knowledge representation languages, that enable us to perform common sense reasoning. For example, OWL supports the open world assumption (OWA), which means that knowledge about everything is assumed to be possibly incomplete at any point of time. However, experience has shown that there are situations that require us to assume that certain parts of the knowledge base are complete. Employing the Closed World Assumption (CWA) helps us achieve this. Circumscription is a very well-known approach towards CWA, which provides closed world semantics by employing the idea of minimal models with respect to certain predicates which are closed. We provide the formal semantics of the notion of Grounded Circumscription, which is an extension of circumscription with desirable properties like decidability. We also provide a tableaux calculus to reason over knowledge bases under the notion of grounded circumscription. Another form of common sense logic, is default logic. Default logic provides a way to specify rules that, by default, hold in most cases but not necessarily in all cases. The classic example of such a rule is: If something is a bird then it flies. The power of defaults comes from the ability of the logic to handle exceptions to the default rules. For example, a bird will be assumed to fly by default unless it is an exception, i.e. it belongs to a class of birds that do not fly, like penguins. Interestingly, this property of defaults can be utilized to create mappings between concepts of different ontologies (knowledge bases). We provide a new semantics for the integration of defaults in description logics and show that it improves upon previously known results in literature. In this study, we give various examples to show the utility and advantages of using a default logic based ontology alignment language. We provide the semantics and decidability results of a default based mapping language for tractable fragments of description logics (or OWL). Furthermore, we provide a proof of concept system and qualitative analysis of the results obtained from the system when compared to that of traditional mapping repair techniques.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Robert A. Arko</style></author><author><style face="normal" font="default" size="100%">Matthew Jones</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Douglas Fils</style></author><author><style face="normal" font="default" size="100%">Tom Narock</style></author><author><style face="normal" font="default" size="100%">Robert Groman</style></author><author><style face="normal" font="default" size="100%">Margaret O'Brien</style></author><author><style face="normal" font="default" size="100%">Evan W. Patton</style></author><author><style face="normal" font="default" size="100%">Danie Kinkade</style></author><author><style face="normal" font="default" size="100%">Shannon Rauch</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linked Data: Forming Partnerships at the Data Layer</style></title><secondary-title><style face="normal" font="default" size="100%">2015 American Geophysical Union Fall Meeting, San Francisco, 14-18 December 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">Sangwon Suh</style></author><author><style face="normal" font="default" size="100%">Bo Pedersen Weidema</style></author><author><style face="normal" font="default" size="100%">Beatriz Rivela</style></author><author><style face="normal" font="default" size="100%">Johan Tivander</style></author><author><style face="normal" font="default" size="100%">David E. Meyer</style></author><author><style face="normal" font="default" size="100%">Gary Berg-Cross</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Wesley Ingwersen</style></author><author><style face="normal" font="default" size="100%">Brandon Kuczenski</style></author><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author><author><style face="normal" font="default" size="100%">Yiting Ju</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Minimal Ontology Pattern for Life Cycle Assessment Data</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP2015) co-located with the 14th International Semantic Web Conference {(ISWC} 2015), Bethlehem, PA, USA, October 11, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Tarek R. Besold</style></author><author><style face="normal" font="default" size="100%">Luc De Raedt</style></author><author><style face="normal" font="default" size="100%">Peter Földiak</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Thomas Icard</style></author><author><style face="normal" font="default" size="100%">Kai-Uwe Kühnberger</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author><author><style face="normal" font="default" size="100%">Riisto Miikkulainen</style></author><author><style face="normal" font="default" size="100%">Daniel L. Silver</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural-Symbolic Learning and Reasoning: Contributions and Challenges</style></title><secondary-title><style face="normal" font="default" size="100%">AAAI 2015 Spring Symposium on Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches. Technical Report SS-15-03, AAAI Press, Palo Alto</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">AAAI Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anne Thessen</style></author><author><style face="normal" font="default" size="100%">Benjamin Fertig</style></author><author><style face="normal" font="default" size="100%">Ramona Walls</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Rick Ziegler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontological Support of Data Discovery and Synthesis in Estuarine and Coastal Science</style></title><secondary-title><style face="normal" font="default" size="100%">CERF 2015: 23rd Biennial Confernence, Grand Challenges in Coastal and Estuarine Science: Securing Our Future, Portland, OR, November 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Víctor Rodríguez-Doncel</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author><author><style face="normal" font="default" size="100%">Ashley Coleman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Chess Games</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP2015) co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, PA, USA</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1461/WOP2015_pattern_abstract_2.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1461</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patrick Obrien</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Jeff Mixter</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Data Integration in the Library Domain</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP2015) co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, PA, USA, October 11, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A university’s institutional repository (IR) contains the in- tellectual output of its faculty, staff and students. Its content is exten- sive and heterogenous, which complicates data aggregation and discovery tasks. To address these challenges, we propose the use of a conceptual ontology design pattern to model information for the IR domain which is general enough to be reused across different IR datasets.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Holly Ferguson</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Monika Solanki</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Dynamic Relative Relationships</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP 2015) co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pensylvania, USA, October 11, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1461/WOP2015_paper_3.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1461</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Sunje Dallmeir-Tiessen</style></author><author><style face="normal" font="default" size="100%">Patricia Herterich</style></author><author><style face="normal" font="default" size="100%">Michael D. Hildreth</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Kati Lassila-Perini</style></author><author><style face="normal" font="default" size="100%">Elizabeth Sexton-Kennedy</style></author><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author><author><style face="normal" font="default" size="100%">Gordon Watts</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Monika Solanki</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Particle Physics Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP 2015) co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pensylvania, USA, October 11, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1461/WOP2015_pattern_abstract_5.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1461</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The detector final state is the core element of particle physics analysis as it defines the physical characteristics that form the basis of the measurement presented in a published paper. Although they are a crucial part of the research process, detector final states are not yet formally described, published in papers or searchable in a convenient way. This paper aims at providing an ontology pattern for the detector final state that can be used as a building block for an ontology covering the whole particle physics analysis life cycle.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Matthew Jones</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Audrey Mickle</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author><author><style face="normal" font="default" size="100%">Douglas Fils</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Design Patterns: Bridging the Gap Between Local Semantic Use Cases and Large-Scale, Long-Term Data Integration</style></title><secondary-title><style face="normal" font="default" size="100%">European Geosciences Union General Assembly 2015, Vienna, Austria, 12 - 17 April 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brandon Kuczenski</style></author><author><style face="normal" font="default" size="100%">Wesley Ingwersen</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Gary Berg-Cross</style></author><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author><author><style face="normal" font="default" size="100%">Sangwon Suh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Design Patterns for Semantically Enriched LCA</style></title><secondary-title><style face="normal" font="default" size="100%">LCA XV, University of British Columbia, Vancouver, Canada, October 6-8, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bo Yan</style></author><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">Brandon Kuczenski</style></author><author><style face="normal" font="default" size="100%">Krzsyztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Andrea Ballatore</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Sangwon Suh</style></author><author><style face="normal" font="default" size="100%">Wesley Ingwersen</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Claudia d'Amato</style></author><author><style face="normal" font="default" size="100%">Freddy Lécué</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Fabian Wirth</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology For Specifying Spatiotemporal Scopes in Life Cycle Assessment</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1501/Diversity2015-paper_4.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1501</style></volume><pages><style face="normal" font="default" size="100%">25-30</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology modeling with domain experts</style></title><secondary-title><style face="normal" font="default" size="100%">1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology modeling with domain experts: The GeoVoCamp experience</style></title><secondary-title><style face="normal" font="default" size="100%">Diversity++ 2015, Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Pattern Modeling for Cross-Repository Data Integration in the Ocean Sciences: The Oceanographic Cruise Example</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web in Earth and Space Science: Current Status and Future Directions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pages><style face="normal" font="default" size="100%">256-284</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">EarthCube is a major effort of the National Science Foundation to establish a next-generation knowledge architecture for the broader geosciences. Data storage, retrieval, access, and reuse are central parts of this new effort. Currently, EarthCube is organized around several building blocks and research coordination networks. OceanLink is a semantics-enabled building block that aims at improving data retrieval and reuse via ontologies, Semantic Web technologies, and Linked Data for the ocean sciences. Cruises, in the sense of research expeditions, are central events for ocean scientists. Consequently, information about these cruises and the involved vessels is of primary interest for oceanographers, and thus, needs to be shared and made retrievable. In this paper, we report the use of a design pattern-centric strategy to model Cruise for OceanLink data integration. We provide a formal axiomatization of the introduced pattern using the Web Ontology Language, explain design choices and discuss the planned deployment and application scenarios of our model.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Pattern-Based Data Integration</style></title><secondary-title><style face="normal" font="default" size="100%">Department of Computer Science and Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%"> http://rave.ohiolink.edu/etdc/view?acc_num=wright1453177798</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Wright State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Dayton</style></pub-location><volume><style face="normal" font="default" size="100%">Doctor of Philosophy</style></volume><pages><style face="normal" font="default" size="100%">233</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;page&quot; title=&quot;Page 3&quot;&gt;
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&lt;p&gt;&lt;span&gt;Data integration is concerned with providing a unified access to data residing at multiple sources. Such a unified access is realized by having a global schema and a set of mappings between the global schema and the local schemas of each data source, which specify how user queries at the global schema can be translated into queries at the local schemas. Data sources are typically developed and maintained independently, and thus, highly heterogeneous. This causes difficulties in integration because of the lack of interoperability in the aspect of architecture, data format, as well as syntax and semantics of the data. &lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span&gt;This dissertation represents a study on how small, self-contained ontologies, called ontology design patterns, can be employed to provide semantic interoperability in a cross-repository data integration system. The idea of this so-called ontology pattern- based data integration is that a collection of ontology design patterns can act as the global schema that still contains sufficient semantics, but is also flexible and simple enough to be used by linked data providers. On the one side, this differs from existing ontology-based solutions, which are based on large, monolithic ontologies that provide very rich semantics, but enforce too restrictive ontological choices, hence are shunned by many data providers. On the other side, this also differs from the purely linked data based solutions, which do offer simplicity and flexibility in data publishing, but too little in terms of semantic interoperability. &lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span&gt;We demonstrate the feasibility of this idea through the actual development of a large scale data integration project involving seven ocean science data repositories from five institutions in the U.S. In addition, we make two contributions as part of this dissertation work, which also play crucial roles in the aforementioned data integration project. First, we develop a collection of more than a dozen ontology design patterns that capture the key notions in the ocean science occurring in the participating data repositories. These patterns contain axiomatization of the key notions and were developed with an intensive involvement from the domain experts. Modeling of the patterns was done in a systematic workflow to ensure modularity, reusability, and flexibility of the whole pattern collection. Second, we propose the so-called pattern views that allow data providers to publish their data in very simple intermediate schema and show that they can greatly assist data providers to publish their data without requiring a thorough understanding of the axiomatization of the patterns.&amp;nbsp;&lt;/span&gt;&lt;/p&gt;
&lt;/div&gt;
&lt;/div&gt;
&lt;/div&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Dissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Víctor Rodríguez-Doncel</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Reihaneh Amini</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Olaf Hartig</style></author><author><style face="normal" font="default" size="100%">Juan Sequeda</style></author><author><style face="normal" font="default" size="100%">Aidan Hogan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Pattern-Based Linked Data Publication: The Linked Chess Dataset Case</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 6th International Workshop on Consuming Linked Data co-located with 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, US, October 12th, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dase.cs.wright.edu/publications/pattern-based-linked-data-publication-linked-chess-dataset-case</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1426</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper discusses the relationship between ontology design patterns (ODPs), data models and linked data, proposing a method that simplifies the task of publishing linked data while adhering to good modeling practices that reuse well-studied ODPs. The proposed process simplifies the tasks of the domain experts but preserves the integrity of the design patterns, favoring a well-designed and well documented data model which fosters data reuse. The work is illustrated with a linked dataset of two million chess games, with the key information mapped to other linked datasets and supported by formalized design patterns. This is the first time a chess dataset is presented as linked data, and an insight on its usefulness is given.&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Technical Report</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Claudia d'Amato</style></author><author><style face="normal" font="default" size="100%">Freddy Lécué</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Tom Narock</style></author><author><style face="normal" font="default" size="100%">Fabian Wirth</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015</style></title><secondary-title><style face="normal" font="default" size="100%">CEUR Workshop Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1501</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Bethlehem, PA, USA</style></pub-location><volume><style face="normal" font="default" size="100%">1501</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Tom Narock</style></author><author><style face="normal" font="default" size="100%">Monika Solanki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings of the 6th Workshop on Ontology and Semantic Web Patterns (WOP 2015) co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pensylvania, USA, October 11, 2015</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Peng Ji</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Claudia d'Amato</style></author><author><style face="normal" font="default" size="100%">Freddy Lécué</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Fabian Wirth</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">R2R+BCO-DMO – Linked Oceanographic Datasets</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1501</style></volume><pages><style face="normal" font="default" size="100%">15-24</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Biological and Chemical Oceanography Data Management Office (BCO-DMO) and the Rolling Deck to Repository (R2R) program are two key data repositories for oceanographic research, supported by the U.S. National Science Foundation (NSF). R2R curates digital data and documentation generated by environmental sensor systems installed on vessels from the U.S. academic research fleet, with support from the NSF Oceanographic Technical Services and Arctic Research Logistics Programs. BCO-DMO human-curates and maintains data and metadata including biological, chemical, and physical measurements and results from projects funded by the NSF Biological Oceanography, Chemical Oceanography, and Antarctic Organisms &amp; Ecosystems Programs. These two repositories have a strong connection, and document several thousand U.S. oceanographic research expeditions since the 1970’s. Recently, R2R and BCO-DMO have made their metadata collections available as Linked Data, accessible via public SPARQL endpoints. In this paper, we report on these datasets.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zohreh Alavi</style></author><author><style face="normal" font="default" size="100%">Sagar Sharma</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">Keke Chen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Scalable Euclidean Embedding for Big Data</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Cloud Computing </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE </style></publisher><pub-location><style face="normal" font="default" size="100%">New York City, NY </style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Semantic Web Journal as Linked Data</style></title><secondary-title><style face="normal" font="default" size="100%">International Semantic Web Conference, ISWC 2015, Posters and Demonstrations Track</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Semantic Web Journal Review Process: Transparent and Open</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Computer Society Special Technical Community on Social Networking E-Letter</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2015</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantics for Big Data</style></title><secondary-title><style face="normal" font="default" size="100%">AI Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ojs/index.php/aimagazine/article/view/2559</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">3–4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kasthuri Jayarajah</style></author><author><style face="normal" font="default" size="100%">Shuochao Yao</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Archan Misra</style></author><author><style face="normal" font="default" size="100%">Geeth De Mel</style></author><author><style face="normal" font="default" size="100%">Julie Skipper</style></author><author><style face="normal" font="default" size="100%">Tarek Abdelzaher</style></author><author><style face="normal" font="default" size="100%">Michael Kolodny</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Social Signal Processing for Real-time Situational Understanding: a Vision and Approach</style></title><secondary-title><style face="normal" font="default" size="100%">First International Workshop on Social Sensing (SocialSens 2015)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pub-location><style face="normal" font="default" size="100%">Dallas, Texas, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;p1&quot;&gt;The US Army Research Laboratory (ARL) and&amp;nbsp;the Air Force Research Laboratory (AFRL) have established a&amp;nbsp;collaborative research enterprise referred to as the Situational&amp;nbsp;Understanding Research Institute (SURI). The goal is to develop&amp;nbsp;an information processing framework to help the military obtain&amp;nbsp;real-time situational awareness of physical events by harnessing&amp;nbsp;the combined power of multiple sensing sources to obtain insights&amp;nbsp;about events and their evolution. It is envisioned that one could&amp;nbsp;use such information to predict behaviors of groups, be they local&amp;nbsp;transient groups (e.g., protests) or widespread, networked groups,&amp;nbsp;and thus enable proactive prevention of nefarious activities. This&amp;nbsp;paper presents a vision of how social media sources can be&amp;nbsp;exploited in the above context to obtain insights about events,&amp;nbsp;groups, and their evolution.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Prabhaker Mateti</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Rule Based Distributed OWL Reasoning Framework</style></title><secondary-title><style face="normal" font="default" size="100%">12th OWL Experiences and Directions Workshop (OWLED 2015) co-located with the 14th International Semantic Web Conference (ISWC 2015)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Bethlehem, PA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The amount of data exposed in the form of RDF and OWL continues to increase exponentially. Some approaches have already been proposed for the scalable reasoning over several language profiles such as RDFS, OWL Horst, OWL 2 EL, OWL 2 RL etc. But all those approaches are limited to the particular ruleset that the reasoner supports. In this work, we propose the idea for a rule-based distributed reasoning framework that can support any given ruleset and highlight some of the challenges that needs to be solved in order to implement such a framework.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Defeasible Mappings for Tractable Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">ISWC 2015 - 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">237-252</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Why the Data Train Needs Semantic Rails</style></title><secondary-title><style face="normal" font="default" size="100%">AI Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ojs/index.php/aimagazine/article/view/2560</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">5–14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">While catchphrases such as big data, smart data, data intensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, i.e., statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today’s chaotic information universe, how one would understand which datasets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The Semantic Web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights works best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">C. Maria Keet</style></author><author><style face="normal" font="default" size="100%">Valentina A. M. Tamma</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">All But Not Nothing: Left-Hand Side Universals for Tractable OWL Profiles</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 11th International Workshop on OWL: Experiences and Directions (OWLED 2014) co-located with 13th International Semantic Web Conference on (ISWC 2014), Riva del Garda, Italy, October 17-18, 2014.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">Horn Logics</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1265/owled2014_submission_13.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1265</style></volume><pages><style face="normal" font="default" size="100%">97-108</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We show that occurrences of the universal quantifier in the left-hand side of general concept inclusions can be rewritten into EL++ axioms under certain circumstances. I.e., this intuitive modeling feature is available for OWL EL while retaining tractability. Furthermore, this rewriting makes it possible to reason over corresponding extensions of EL++ and Horn-SROIQ using standard reasoners.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Akbari, Elnaz</style></author><author><style face="normal" font="default" size="100%">Buntat, Zolkafle</style></author><author><style face="normal" font="default" size="100%">Enzevaee, Aria</style></author><author><style face="normal" font="default" size="100%">Ebrahimi, Monireh</style></author><author><style face="normal" font="default" size="100%">Yazdavar, Amir Hossein</style></author><author><style face="normal" font="default" size="100%">Yusof, Rubiyah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analytical modeling and simulation of I–V characteristics in carbon nanotube based gas sensors using ANN and SVR methods</style></title><secondary-title><style face="normal" font="default" size="100%">Chemometrics and Intelligent Laboratory Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">137</style></volume><pages><style face="normal" font="default" size="100%">173–180</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Gennady Agre</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Sergei O. Kuznetsov</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Artificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014, Varna, Bulgaria, September 11-13, 2014. Proceedings</style></title><secondary-title><style face="normal" font="default" size="100%">AIMSA 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-10554-3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8722</style></volume><isbn><style face="normal" font="default" size="100%">978-3-319-10553-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Marco Gori</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining Learning and Reasoning for Big Data</style></title><secondary-title><style face="normal" font="default" size="100%">Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Peter Mika</style></author><author><style face="normal" font="default" size="100%">Tania Tudorache</style></author><author><style face="normal" font="default" size="100%">Abraham Bernstein</style></author><author><style face="normal" font="default" size="100%">Chris Welty</style></author><author><style face="normal" font="default" size="100%">Craig A. Knoblock</style></author><author><style face="normal" font="default" size="100%">Denny Vrandecic</style></author><author><style face="normal" font="default" size="100%">Paul T. Groth</style></author><author><style face="normal" font="default" size="100%">Natasha F. Noy</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Carole A. Goble</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Conference v2.0: An uncertain version of the OAEI Conference benchmark</style></title><secondary-title><style face="normal" font="default" size="100%">13th International Semantic Web Conference (ISWC 2014)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">benchmark</style></keyword><keyword><style  face="normal" font="default" size="100%">OAEI</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Alignment</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Lecture Notes in Computer Science, Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Riva del Garda, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">8797</style></volume><pages><style face="normal" font="default" size="100%">148-163</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Ontology Alignment Evaluation Initiative is a set of benchmarks for evaluating the performance of ontology alignment systems. In this paper we re-examine the Conference track of the OAEI, with a focus on the degree of agreement between the reference alignments within this track and the opinion of experts. We propose a new version of this benchmark that more closely corresponds to expert opinion and confidence on the matches. The performance of top alignment systems is compared on both versions of the benchmark. Additionally, a general method for crowdsourcing the development of more benchmarks of this type using Amazon’s Mechanical Turk is introduced and shown to be scalable, cost-effective and to agree well with expert opinion.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Encyclopedia of Social Network Analysis and Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4614-6170-8_108</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">346-351</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Matthias Knorr</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Dov. M. Gabbay</style></author><author><style face="normal" font="default" size="100%">John Woods</style></author><author><style face="normal" font="default" size="100%">Jörg Siekmann</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of the History of Logic</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">679-710</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Prabhaker Mateti</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ruben Verborgh</style></author><author><style face="normal" font="default" size="100%">Erik Mannens</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Developing a Distributed Reasoner for the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ISWC Developers Workshop 2014, co-located with the 13th International Semantic Web Conference (ISWC 2014)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1268/paper18.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Riva del Garda, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">1268</style></volume><pages><style face="normal" font="default" size="100%">108–112</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;OWL 2 EL is one of the tractable profiles of the Web Ontology Language (OWL) which has been standardized by the W3C. OWL 2&amp;nbsp;EL provides suficient expressivity to model large biomedical ontologies&amp;nbsp;as well streaming traffic data. Automated generation of ontologies from&amp;nbsp;streaming data and text can lead to very large ontologies. There is a&amp;nbsp;need to develop scalable reasoning approaches which scale with the size&amp;nbsp;of the ontologies. We briefly describe our distributed reasoner, DistEL&amp;nbsp;along with our experience and lessons learned during its development.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Prabhaker Mateti</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Thorsten Liebig</style></author><author><style face="normal" font="default" size="100%">Achille Fokoue</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Distributed OWL EL Reasoning: The Story So Far</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Distributed Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL EL</style></keyword><keyword><style  face="normal" font="default" size="100%">Scalability</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Riva del Garda, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">1261</style></volume><pages><style face="normal" font="default" size="100%">61-76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires distributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now exist. For OWL 2 EL, several distributed reasoning approaches have been tried, but are all perceived to be inefficient. We analyze this perception. We analyze completion rule based distributed approaches, using different characteristics, such as dependency among the rules, implementation optimizations, how axioms and rules are distributed. We also present a distributed queue approach for the classification of ontologies in description logic EL+ (fragment of OWL 2 EL).&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Cristina Feier</style></author><author><style face="normal" font="default" size="100%">Cuenca Grau, Bernardo</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Ian Horrocks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EL-ifying Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">Automated Reasoning - 7th International Joint Conference, IJCAR 2014, Held as Part of the Vienna Summer of Logic, {VSL} 2014, Vienna, Austria, July 19-22, 2014. Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Rewriting</style></keyword><keyword><style  face="normal" font="default" size="100%">Tractable Reasoning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-08587-6_36</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">464–479</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The OWL 2 profiles are fragments of the ontology language OWL 2 for which standard reasoning tasks are feasible in polynomial time. Many OWL ontologies, however, contain a typically small number of out-of-profile axioms, which may have little or no influence on reasoning outcomes. We investigate techniques for rewriting axioms into the EL and RL profiles of OWL 2. We have tested our techniques on both classification and data reasoning tasks with encouraging results.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Md Kamruzzaman Sarker</style></author><author><style face="normal" font="default" size="100%">Kazi Md Rokibul, Alam</style></author><author><style face="normal" font="default" size="100%">Md Arifuzzaman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Emotion recognition from speech based on relevant feature and majority voting</style></title><secondary-title><style face="normal" font="default" size="100%">ICIEV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper proposes an approach to detect emotion from human speech employing majority voting technique over several machine learning techniques. The contribution of this work is in two folds: firstly it selects those features of speech which is most promising for classification and secondly it uses the majority voting technique that selects the exact class of emotion. Here, majority voting technique has been applied over Neural Network (NN), Decision Tree (DT), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Input vector of NN, DT, SVM and KNN consists of various acoustic and prosodic features like Pitch, Mel-Frequency Cepstral coefficients etc. From speech signal many feature have been extracted and only promising features have been selected. To consider a feature as promising, Fast Correlation based feature selection (FCBF) and Fisher score algorithms have been used and only those features are selected which are highly ranked by both of them. The proposed approach has been tested on Berlin dataset of emotional speech [3] and Electromagnetic Articulography (EMA) dataset [4]. The experimental result shows that majority voting technique attains better accuracy over individual machine learning techniques. The employment of the proposed approach can effectively recognize the emotion of human beings in case of social robot, intelligent chat client, call-center of a company etc.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Robert Groman</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Molly Allison</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Yu Chen</style></author><author><style face="normal" font="default" size="100%">Peter Fox</style></author><author><style face="normal" font="default" size="100%">David Glover</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adam Leadbetter</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Patrick West</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enhancing Ocean Research Data Access</style></title><secondary-title><style face="normal" font="default" size="100%">European Geosciences Union General Assembly 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pub-location><style face="normal" font="default" size="100%">Vienna, Austria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Benjamin Adams</style></author><author><style face="normal" font="default" size="100%">Dave Kolas</style></author><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Five stars of Linked Data vocabulary use</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-140135</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">173–176</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In 2010 Tim Berners-Lee introduced a 5 star rating to his Linked Data design issues page to encourage data publishers along the road to good Linked Data. What makes the star rating so effective is its simplicity, clarity, and a pinch of psychology – is your data 5 star? While there is an abundance of 5 star Linked Data available today, finding, querying, and integrating/interlinking these data is, to say the least, difficult. While the literature has largely focused on describing datasets, e.g., by adding provenance information, or interlinking them, e.g., by co-reference resolution tools, we would like to take Berners-Lee’s original proposal to the next level by introducing a 5 star rating for Linked Data vocabulary use.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph Pinkel</style></author><author><style face="normal" font="default" size="100%">Carsten Binnig</style></author><author><style face="normal" font="default" size="100%">Peter Haase</style></author><author><style face="normal" font="default" size="100%">Clemens Martin</style></author><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Johannes Trame</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Valentina Presutti</style></author><author><style face="normal" font="default" size="100%">Claudia d'Amato</style></author><author><style face="normal" font="default" size="100%">Fabien Gandon</style></author><author><style face="normal" font="default" size="100%">Mathieu d'Aquin</style></author><author><style face="normal" font="default" size="100%">Steffen Staab</style></author><author><style face="normal" font="default" size="100%">Anna Tordai</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">How to Best Find a Partner? An Evaluation of Editing Approaches to Construct R2RML Mappings</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web: Trends and Challenges - 11th International Conference, ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8465</style></volume><pages><style face="normal" font="default" size="100%">675–690</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Jens Lehmann</style></author><author><style face="normal" font="default" size="100%">Axel Polleres</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Dov. M. Gabbay</style></author><author><style face="normal" font="default" size="100%">John Woods</style></author><author><style face="normal" font="default" size="100%">Jörg Siekmann</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Logics for the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of the History of Logic</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">679-710</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Marco Gori</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)</style></title><secondary-title><style face="normal" font="default" size="100%">Dagstuhl Reports</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.4230/DagRep.4.9.50</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">50–84</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The OceanLink Project</style></title><secondary-title><style face="normal" font="default" size="100%">American Geophysical Union Fall Meeting, San Francisco, 15-19 December 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Jimmy Lin</style></author><author><style face="normal" font="default" size="100%">Jian Pei</style></author><author><style face="normal" font="default" size="100%">Xiaohua Hu</style></author><author><style face="normal" font="default" size="100%">Wo Chang</style></author><author><style face="normal" font="default" size="100%">Raghunath Nambiar</style></author><author><style face="normal" font="default" size="100%">Charu Aggarwal</style></author><author><style face="normal" font="default" size="100%">Nick Cercone</style></author><author><style face="normal" font="default" size="100%">Vasant Honavar</style></author><author><style face="normal" font="default" size="100%">Jun Huan</style></author><author><style face="normal" font="default" size="100%">Bamshad Mobasher</style></author><author><style face="normal" font="default" size="100%">Saumyadipta Pyne</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The OceanLink project</style></title><secondary-title><style face="normal" font="default" size="100%">2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, October 27-30, 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6973861</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">{IEEE}</style></publisher><pages><style face="normal" font="default" size="100%">14-21</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4799-5665-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Today's scientific investigations are producing large numbers of scholarly products. These products continue to increase in diversity and complexity as researchers recognize that scholarly achievements are not only published articles but also datasets, software, and associated supporting materials. OceanLink is an online platform that addresses scholarly discovery and collaboration in the ocean sciences. The OceanLink project leverages Semantic Web technologies, web mining, and crowdsourcing to identify links between data centers, digital repositories, and professional societies to enhance discovery, enable collaboration, and begin to assess research contribution.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amin Abdalla</style></author><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Naicong Li</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Activity Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 5th Workshop on Ontology and Semantic Web Patterns (WOP2014) co-located with the 13th International Semantic Web Conference {(ISWC} 2014), Riva del Garda, Italy, October 19, 2014.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Activity</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Design Pattern</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1302/paper8.pdf</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">78–81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Activity is an important concept in many fields, and a number of activity-related ontologies have been developed. While suitable for their designated use cases, these ontologies cannot be easily generalized to other applications. This paper aims at providing a generic ontology design pattern to model the common core of activities in different domains. Such a pattern can be used as a building block to construct more specific activity ontologies.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monica Sam</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Cong Wang</style></author><author><style face="normal" font="default" size="100%">John C. Gallagher</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">de Boer, Victor</style></author><author><style face="normal" font="default" size="100%">Aldo Gangemi</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Agnieszka Lawrynowicz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Cooking Recipes - Classroom Created</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 5th Workshop on Ontology and Semantic Web Patterns (WOP2014) co-located with the 13th International Semantic Web Conference (ISWC 2014), Riva del Garda, Italy, October 19, 2014.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1302</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1302</style></volume><pages><style face="normal" font="default" size="100%">49-60</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a description and result of an ontology modeling process taken to the classroom. The application domain considered was cooking recipes. The modeling goal was to bridge heterogeneity across representational choices by developing a content ontology design pattern which is general enough to allow for the integration of information from different web sites. We will discuss the pattern developed, and report on corresponding insights and lessons learned.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Holly Ferguson</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Aimee Buccellato</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">Gary Berg-Cross</style></author><author><style face="normal" font="default" size="100%">Torsten Hahmann</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">de Boer, Victor</style></author><author><style face="normal" font="default" size="100%">Aldo Gangemi</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Agnieszka Lawrynowicz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Material Transformation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 5th Workshop on Ontology and Semantic Web Patterns (WOP2014) co-located with the 13th International Semantic Web Conference (ISWC 2014), Riva del Garda, Italy, October 19, 2014.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1302</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1302</style></volume><pages><style face="normal" font="default" size="100%">73-77</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work we discuss an ontology design pattern for material transformations. It models the relation between products, resources, and catalysts in the transformation process. Our axiomatization goes beyond a mere surface semantics. While we focus on the construction domain, the pattern can also be applied to chemistry and other domains.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Stefan Schlobach</style></author><author><style face="normal" font="default" size="100%">Patrick Lambrix</style></author><author><style face="normal" font="default" size="100%">Eero Hyvönen</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Design Patterns for Large-Scale Data Interchange and Discovery</style></title><secondary-title><style face="normal" font="default" size="100%">19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Linköping, Sweden</style></pub-location><volume><style face="normal" font="default" size="100%">8876</style></volume><pages><style face="normal" font="default" size="100%">XIX-XX</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Aldo Gangemi</style></author><author><style face="normal" font="default" size="100%">Verena V. Hafner</style></author><author><style face="normal" font="default" size="100%">Werner Kuhn</style></author><author><style face="normal" font="default" size="100%">Simon Scheider</style></author><author><style face="normal" font="default" size="100%">Luc Steels</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Design Patterns for Ocean Science Data Discovery</style></title><secondary-title><style face="normal" font="default" size="100%">Spatial reference in the Semantic Web and in Robotics (Dagstuhl Seminar 14142)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Pattern for Oceanograhic Cruises: Towards an Oceanographer's Dream of Integrated Knowledge Discovery</style></title><secondary-title><style face="normal" font="default" size="100%">OceanLink Technical Report</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;EarthCube is a major effort of the National Science Foundation to establish a next-generation knowledge architecture for the broader geosciences. Data storage, retrieval, access, and reuse are central parts of this new effort. Currently, EarthCube is organized around several building blocks and research coordination networks. OceanLink is a semanticsenabled building block that aims at improving data retrieval and reuse via ontologies, Semantic Web technologies, and Linked Data for the ocean sciences. Cruises, in the sense of research expeditions, are central events for ocean scientists. Consequently, information about these cruises and the involved vessels has to be shared and made retrievable. For example, the ability to find cruises in the vicinity of physiographic features of interest, e.g., a hydrothermal vent field or a fracture zone, is of primary interest for oceanographers. In this paper, we use a design pattern-centric strategy to engineer ontologies for OceanLink. We provide a formal axiomatization of the introduced patterns and ontologies using the Web Ontology Language, explain design choices, discuss the re-usability of our models, and provide lessons learned for the future geo-ontologies.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Properties of Property Alignment</style></title><secondary-title><style face="normal" font="default" size="100%">Ninth International Workshop on Ontology Matching</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pub-location><style face="normal" font="default" size="100%">Riva del Garda, Italy</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Douglas Fils</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Matthew Jones</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Kerstin Lehnert</style></author><author><style face="normal" font="default" size="100%">Audrey Mickle</style></author><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Provenance Usage in the OceanLink Project</style></title><secondary-title><style face="normal" font="default" size="100%">American Geophysical Union Fall Meeting, San Francisco, 15-19 December 2014.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Cristina Feier</style></author><author><style face="normal" font="default" size="100%">Cuenca Grau, Bernardo</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Ian Horrocks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pushing the Boundaries of Tractable Ontology Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Tractable Reasoning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-11915-1_10</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">148–163</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We identify a class of Horn ontologies for which standard reasoning tasks such as instance checking and classification are tractable. The class is general enough to include the OWL 2 EL, QL, and RL profiles. Verifying whether a Horn ontology belongs to the class can be done in polynomial time. We show empirically that the class includes many real-world ontologies that are not included in any OWL 2 profile, and thus that polynomial time reasoning is possible for these ontologies.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zohreh Alavi</style></author><author><style face="normal" font="default" size="100%">Lu Zhou</style></author><author><style face="normal" font="default" size="100%">James Powers</style></author><author><style face="normal" font="default" size="100%">Keke Chen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">RASP-QS: Efficient and Confidential Query Services in the Cloud</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pub-location><style face="normal" font="default" size="100%">VLDB Endowment</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cong Wang</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">Encyclopedia of Social Network Analysis and Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4614-6170-8_115</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1499–1501</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gully Burns</style></author><author><style face="normal" font="default" size="100%">Yolanda Gil</style></author><author><style face="normal" font="default" size="100%">Yan Liu</style></author><author><style face="normal" font="default" size="100%">Natalia Villanueva-Rosales</style></author><author><style face="normal" font="default" size="100%">Sebastian Risi</style></author><author><style face="normal" font="default" size="100%">Joel Lehman</style></author><author><style face="normal" font="default" size="100%">Jeff Clune</style></author><author><style face="normal" font="default" size="100%">Christian Lebiere</style></author><author><style face="normal" font="default" size="100%">Paul S. Rosenbloom</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Samarth Swarup</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reports on the 2013 AAAI Fall Symposium Series</style></title><secondary-title><style face="normal" font="default" size="100%">AI Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ojs/index.php/aimagazine/article/view/2538</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">69–74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">T. Supnithi</style></author><author><style face="normal" font="default" size="100%">T. Yamaguchi</style></author><author><style face="normal" font="default" size="100%">Jeff Z. Pan</style></author><author><style face="normal" font="default" size="100%">V. Wuwongse</style></author><author><style face="normal" font="default" size="100%">M. Buranarach</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Revisiting default description logics – and their role in aligning ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Technology, 4th Joint International Conference, JIST 2014</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">default logic</style></keyword><keyword><style  face="normal" font="default" size="100%">defaults</style></keyword><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Alignment</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Lecture Notes in Computer Science, Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Chiang Mai, Thailand</style></pub-location><volume><style face="normal" font="default" size="100%">8943</style></volume><pages><style face="normal" font="default" size="100%">3-18</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new approach to extend the Web Ontology Language (OWL) with the capabilities to reason with defaults. This work improves upon the previously established results on integrating defaults with description logics (DLs), which were shown to be decidable only when the application of defaults is restricted to named individuals in the knowledge base. We demonstrate that the application of defaults (integrated with DLs) does not have to be restricted to named individuals to retain decidability and elaborate on the application of defaults in the context of ontology alignment and ontology-based systems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Yanning Chen</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Robert Groman</style></author><author><style face="normal" font="default" size="100%">Shannon Rauch</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Entity Pairing for Improved Data Validation and Discovery</style></title><secondary-title><style face="normal" font="default" size="100%"> European Geosciences Union General Assembly 2014, Vienna, Austria, 27 April - 02 May 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2014</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">2476</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;One of the central incentives for linked data implementations is the opportunity to leverage the rich logic inherent in structured data. The logic embedded in semantic models can strengthen capabilities for data discovery and data validation when pairing entities from distinct, contextually-related datasets. The creation of links between the two datasets broadens data discovery by using the semantic logic to help machines compare similar entities and properties that exist on different levels of granularity. This semantic capability enables appropriate entity pairing without making inaccurate assertions as to the nature of the relationship. Entity pairing also provides a context to accurately validate the correctness of an entity's property values - an exercise highly valued by data management practices who seek to ensure the quality and correctness of their data. The Biological and Chemical Oceanography Data Management Office (BCO-DMO) semantically models metadata surrounding oceanographic researchcruises, but other sources outside of BCO-DMO exist that also model metadata about these same cruises. For BCO-DMO, the process of successfully pairing its entities to these sources begins by selecting sources that are decidedly trustworthy and authoritative for the modeled concepts. In this case, the Rolling Deck to Repository (R2R) program has a well-respected reputation among the oceanographic research community, presents a data context that is uniquely different and valuable, and semantically models its cruise metadata. Where BCO-DMO exposes the processed, analyzed data products generated by researchers, R2R exposes the raw shipboard data that was collected on the same research cruises. Interlinking these cruise entities expands data discovery capabilities but also allows for validating the contextual correctness of both BCO-DMO's and R2R's cruise metadata. Assessing the potential for a link between two datasets for a similar entity consists of aligning like properties and deciding on the appropriate semantic markup to describe the link. This highlights the desire for research organizations like BCO-DMO and R2R to ensure the complete accuracy of their exposed metadata, as it directly reflects on their reputations as successful and trustworthy source of research data. Therefore, data validation reaches beyond simple syntax of property values into contextual correctness. As a human process, this is a time-intensive task that does not scale well for finite human and funding resources. Therefore, to assess contextual correctness across datasets at different levels of granularity, BCO-DMO is developing a system that employs semantic technologies to aid the human process by organizing potential links and calculating a confidence coefficient as to the correctness of the potential pairing based on the distance between certain entity property values. The system allows humans to quickly scan potential links and their confidence coefficients for asserting persistence and correcting and investigating misaligned entity property values.&lt;/p&gt;
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size="100%">2014</style></year></dates><edition><style face="normal" font="default" size="100%">3</style></edition><publisher><style face="normal" font="default" size="100%">Chapman and Hall/CRC</style></publisher><volume><style face="normal" font="default" size="100%">I</style></volume><pages><style face="normal" font="default" size="100%">50-1 - 50-13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leo Obrst</style></author><author><style face="normal" font="default" size="100%">Michael Grüninger</style></author><author><style face="normal" font="default" size="100%">Ken Baclawski</style></author><author><style face="normal" font="default" size="100%">Mike Bennett</style></author><author><style face="normal" font="default" size="100%">Dan Brickley</style></author><author><style face="normal" font="default" size="100%">Gary Berg-Cross</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Christine Kapp</style></author><author><style face="normal" font="default" size="100%">Oliver Kutz</style></author><author><style face="normal" font="default" size="100%">Christoph Lange</style></author><author><style face="normal" font="default" size="100%">Anatoly Levenchuk</style></author><author><style face="normal" font="default" size="100%">Francesca Quattri</style></author><author><style face="normal" font="default" size="100%">Alan Rector</style></author><author><style face="normal" font="default" size="100%">Todd Schneider</style></author><author><style face="normal" font="default" size="100%">Simon Spero</style></author><author><style face="normal" font="default" size="100%">Anne Thessen</style></author><author><style face="normal" font="default" size="100%">Marcela Vegetti</style></author><author><style face="normal" font="default" size="100%">Amanda Vizedom</style></author><author><style face="normal" font="default" size="100%">Andrea Westerinen</style></author><author><style face="normal" font="default" size="100%">Matthew West</style></author><author><style face="normal" font="default" size="100%">Peter Yim</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Web and Big Data meets Applied Ontology - The Ontology Summit 2014</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Ontology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" 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font="default" size="100%">Ali, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transmission of data with orthogonal frequency division multiplexing technique for communication networks using GHz frequency band soliton carrier</style></title><secondary-title><style face="normal" font="default" size="100%">IET Communications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">1364–1373</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lisa Raymond</style></author><author><style face="normal" font="default" size="100%">Adam Shepherd</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Suzanne Carbotte</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Douglas Fils</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Matthew Jones</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Kerstin Lehnert</style></author><author><style face="normal" font="default" size="100%">Audrey Mickle</style></author><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Peter Wiebe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using Linked Open Data and Semantic Integration to Search Across Geoscience Repositories.</style></title><secondary-title><style face="normal" font="default" size="100%">American Geophysical Union Fall Meeting, San Francisco, 15-19 December 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Reda Alhajj</style></author><author><style face="normal" font="default" size="100%">Jon Rokna</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Web Ontology Language (OWL)</style></title><secondary-title><style face="normal" font="default" size="100%">Encyclopedia of Social Network Analysis and Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pages><style face="normal" font="default" size="100%">2374–2378</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Cristina Feier</style></author><author><style face="normal" font="default" size="100%">Ana Armas Romero</style></author><author><style face="normal" font="default" size="100%">Cuenca Grau, Bernardo</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Ian Horrocks</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Is Your Ontology as Hard as You Think? Rewriting Ontologies into Simpler DLs</style></title><secondary-title><style face="normal" font="default" size="100%">Informal Proceedings of the 27th International Workshop on Description Logics, Vienna, Austria, July 17-20, 2014.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Tractable Reasoning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1193/paper_75.pdf</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">128–140</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We investigate cases where an ontology expressed in a seemingly hard DL can be polynomially reduced to one in a simpler logic, while preserving reasoning outcomes for classification and fact entailment. Our transformations target the elimination of inverse roles, universal and existential restrictions, and in the best case allow us to rewrite the given ontology into one of the OWL 2 profiles. Even if an ontology cannot be fully rewritten into a profile, in many cases our transformations allow us to exploit further optimisation techniques. Moreover, the elimination of some out-of-profile axioms can improve the performance of modular reasoners, such as MORe. We have tested our techniques on both classification and data reasoning tasks with encouraging results.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sarasi Lalithsena</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Prateek Jain</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic Domain Identification for Linked Open Data</style></title><secondary-title><style face="normal" font="default" size="100%">2013 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2013</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dataset search</style></keyword><keyword><style  face="normal" font="default" size="100%">Domain Identification</style></keyword><keyword><style  face="normal" font="default" size="100%">Linked Open Data Cloud</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1109/WI-IAT.2013.206</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Atlanta, GA, USA</style></pub-location><pages><style face="normal" font="default" size="100%">205–212</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Lise Getoor</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Natasha Noy</style></author><author><style face="normal" font="default" size="100%">Deborah McGuinness</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Bridging KR and Machine Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Final Report on the 2013 NSF Workshop on Research Challenges and Opportunities in Knowledge Representation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Arlington, VA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Natasha Noy</style></author><author><style face="normal" font="default" size="100%">Deborah McGuinness</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Bridging open-world knowledge and closed-world data</style></title><secondary-title><style face="normal" font="default" size="100%">Final Report on the 2013 NSF Workshop on Research Challenges and Opportunities in Knowledge Representation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Arlington, VA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Complexities of Horn Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Trans. Comput. Log.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computational complexity</style></keyword><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">Horn logic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/2422085.2422087</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">2</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Description Logics (DLs) have become a prominent paradigm for representing knowledge bases in a variety of application areas. Central to leveraging them for corresponding systems is the provision of a favourable balance between expressivity of the knowledge representation formalism on the one hand, and runtime performance of reasoning algorithms on the other. Due to this, Horn description logics (Horn DLs) have attracted attention since their (worst-case) data complexities are in general lower than their overall (i.e. combined) complexities, which makes them attractive for reasoning with large sets of instance data (ABoxes). However, the natural question whether Horn DLs also provide advantages for schema (TBox) reasoning has hardly been addressed so far. In this paper, we therefore provide a thorough and comprehensive analysis of the combined complexities of Horn DLs. While the combined complexity for many Horn DLs studied herein turns out to be the same as for their non-Horn counterparts, we identify subboolean DLs where Hornness simplifies reasoning. We also provide convenient normal forms for Horn DLs.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tom Narock</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">Jim Hendler</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Crowdsourcing Semantics for Big Data in Geoscience Applications</style></title><secondary-title><style face="normal" font="default" size="100%">Semantics for Big Data: Papers from the AAAI Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2013</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Arlington, Virginia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Prabhaker Mateti</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Thorsten Liebig</style></author><author><style face="normal" font="default" size="100%">Achille Fokoue</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">DistEL: A Distributed EL+ Ontology Classifier</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, co-located with the International Semantic Web Conference (ISWC 2013)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">DistEL</style></keyword><keyword><style  face="normal" font="default" size="100%">Distributed Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">EL+</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Scalability</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2013</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Sydney, Australia</style></pub-location><volume><style face="normal" font="default" size="100%">1046</style></volume><pages><style face="normal" font="default" size="100%">17-32</style></pages><abstract><style face="normal" font="default" size="100%">OWL 2 EL ontologies are used to model and reason over data from diverse domains such as biomedicine, geography and road traffic. Data in these domains is increasing at a rate quicker than the increase in main memory and computation power of a single machine. Recent efforts in OWL reasoning algorithms lead to the decrease in classification time from several hours to a few seconds even for large ontologies like SNOMED CT. This is especially true for ontologies in the description logic EL+ (a fragment of the OWL 2 EL profile). Reasoners such as Pellet, Hermit, ELK etc. make an assumption that the ontology would fit in the main memory, which is unreasonable given projected increase in data volumes. Increase in the data volume also necessitates an increase in the computation power. This lead us to the use of a distributed system, so that memory and computation requirements can be spread across machines. We present a distributed system for the classification of EL+ ontologies along with some results on its scalability and performance.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Sherif Sakr</style></author><author><style face="normal" font="default" size="100%">Alessandra Sala</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">Tudor Groza</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">D-SPARQ: Distributed, Scalable and Efficient RDF Query Engine</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ISWC 2013 Posters &amp; Demonstrations Track</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">D-SPARQ</style></keyword><keyword><style  face="normal" font="default" size="100%">Distributed Querying</style></keyword><keyword><style  face="normal" font="default" size="100%">Scalable RDF querying</style></keyword><keyword><style  face="normal" font="default" size="100%">SPARQL</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1035/iswc2013_poster_21.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Sydney, Australia</style></pub-location><volume><style face="normal" font="default" size="100%">1035</style></volume><pages><style face="normal" font="default" size="100%">261–264</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We present D-SPARQ, a distributed RDF query engine that combines the MapReduce processing framework with a NoSQL distributed data store, MongoDB. The performance of processing SPARQL queries mainly depends on the efficiency of handling the join operations between the RDF triple patterns. Our system features two unique characteristics that enable efficiently tackling this challenge: 1) Identifying specific patterns of the input queries that enable improving the performance by running different parts of the query in a parallel mode. 2) Using the triple selectivity information for reordering the individual triples of the input query within the identified query patterns. The preliminary results demonstrate the scalability and efficiency of our distributed RDF query engine.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Peter Haase</style></author><author><style face="normal" font="default" size="100%">Michael Schmidt</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">Tudor Groza</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Editing R2RML Mappings Made Easy</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ISWC 2013 Posters &amp; Demonstrations Track, Sydney, Australia, October 23, 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1035</style></volume><pages><style face="normal" font="default" size="100%">101–104</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Simon Scheider</style></author><author><style face="normal" font="default" size="100%">Werner Kuhn</style></author><author><style face="normal" font="default" size="100%">Gary Berg-Cross</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Mike Dean</style></author><author><style face="normal" font="default" size="100%">Dave Kolas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Geo-ontology Design Pattern for Semantic Trajectories</style></title><secondary-title><style face="normal" font="default" size="100%">Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Ontology Design Pattern</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Trajectory</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-01790-7_24</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">438–456</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Trajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Natasha Noy</style></author><author><style face="normal" font="default" size="100%">Deborah McGuinness</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Grand Challenge: From Big Data to Knowledge</style></title><secondary-title><style face="normal" font="default" size="100%"> Final Report on the 2013 NSF Workshop on Research Challenges and Opportunities in Knowledge Representation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Arlington, VA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge Representation in the Big Data Age.</style></title><secondary-title><style face="normal" font="default" size="100%">NSF Workshop: Research Challenges and Opportunities in Knowledge Representation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Arlington, VA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Narock</style></author><author><style face="normal" font="default" size="100%">Eric A. Rozell</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Robert Arko</style></author><author><style face="normal" font="default" size="100%">Cynthia Chandler</style></author><author><style face="normal" font="default" size="100%">Brian D. Wilson</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Leveraging Crowdsourcing and Linked Open Data for Geoscience Data Sharing and Discovery</style></title><secondary-title><style face="normal" font="default" size="100%">2013 American Geophysical Union Fall Meeting</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">San Francisco, CA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Natasha Noy</style></author><author><style face="normal" font="default" size="100%">Deborah McGuinness</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Lightweight KR</style></title><secondary-title><style face="normal" font="default" size="100%">Final Report on the 2013 NSF Workshop on Research Challenges and Opportunities in Knowledge Representation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Arlington, VA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linked Data, Big Data, and the 4th Paradigm</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-130117</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">233–235</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">It appears to be uncontroversial that Linked Data is part of the Big Data landscape. We even go a bit further and claim that Linked Data is an ideal testbed for researching some key Big Data challenges and to experience the 4th paradigm of science in action.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Grant McKenzie</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">Tudor Groza</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Linked Scientometrics: Designing Interactive Scientometrics with Linked Data and Semantic Web Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ISWC 2013 Posters &amp; Demonstrations Track, Sydney, Australia, October 23, 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1035</style></volume><pages><style face="normal" font="default" size="100%">53–56</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yingjie Hu</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Grant McKenzie</style></author><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Harith Alani</style></author><author><style face="normal" font="default" size="100%">Lalana Kagal</style></author><author><style face="normal" font="default" size="100%">Achille Fokoue</style></author><author><style face="normal" font="default" size="100%">Paul T. Groth</style></author><author><style face="normal" font="default" size="100%">Chris Biemann</style></author><author><style face="normal" font="default" size="100%">Josiane Xavier Parreira</style></author><author><style face="normal" font="default" size="100%">Lora Aroyo</style></author><author><style face="normal" font="default" size="100%">Natasha F. Noy</style></author><author><style face="normal" font="default" size="100%">Chris Welty</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Linked-Data-Driven and Semantically-Enabled Journal Portal for Scientometrics</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web - ISWC 2013 - 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21-25, 2013, Proceedings, Part II</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8219</style></volume><pages><style face="normal" font="default" size="100%">114–129</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joshi, Amit Krishna</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Dong, Guozhu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Logical linked data compression</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web: Semantics and Big Data.10th Extended Semantic Web Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">170–184</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The New Manuscript Review System for the Semantic Web Journal</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-130095</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">117</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Simon Scheider</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Charles Vardeman</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Philipp Cimiano</style></author><author><style face="normal" font="default" size="100%">Óscar Corcho</style></author><author><style face="normal" font="default" size="100%">Valentina Presutti</style></author><author><style face="normal" font="default" size="100%">Laura Hollink</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Ontology Design Pattern for Cartographic Map Scaling</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Map Scaling</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Design Patterns</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-38288-8_6</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">7882</style></volume><pages><style face="normal" font="default" size="100%">76–93</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The concepts of scale is at the core of cartographic abstraction and mapping. It defines which geographic phenomena should be displayed, which type of geometry and map symbol to use, which measures can be taken, as well as the degree to which features need to be exaggerated or spatially displaced. In this work, we present an ontology design pattern for map scaling using the Web Ontology Language (OWL) within a particular extension of the OWL RL profile. We explain how it can be used to describe scaling applications, to reason over scale levels, and geometric representations. We propose an axiomatization that allows us to impose meaningful constraints on the pattern, and, thus, to go beyond simple surface semantics. Interestingly, this includes several functional constraints currently not expressible in any of the OWL profiles. We show that for this specific scenario, the addition of such constraints does not increase the reasoning complexity which remains tractable.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Evgeny Kharlamov</style></author><author><style face="normal" font="default" size="100%">Martin Giese</style></author><author><style face="normal" font="default" size="100%">Ernesto Jiménez-Ruiz</style></author><author><style face="normal" font="default" size="100%">Martin G. Skjæveland</style></author><author><style face="normal" font="default" size="100%">Ahmet Soylu</style></author><author><style face="normal" font="default" size="100%">Dmitriy Zheleznyakov</style></author><author><style face="normal" font="default" size="100%">Timea Bagosi</style></author><author><style face="normal" font="default" size="100%">Marco Console</style></author><author><style face="normal" font="default" size="100%">Peter Haase</style></author><author><style face="normal" font="default" size="100%">Ian Horrocks</style></author><author><style face="normal" font="default" size="100%">Sarunas Marciuska</style></author><author><style face="normal" font="default" size="100%">Christoph Pinkel</style></author><author><style face="normal" font="default" size="100%">Mariano Rodriguez-Muro</style></author><author><style face="normal" font="default" size="100%">Marco Ruzzi</style></author><author><style face="normal" font="default" size="100%">Valerio Santarelli</style></author><author><style face="normal" font="default" size="100%">Domenico Fabio Savo</style></author><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Michael Schmidt</style></author><author><style face="normal" font="default" size="100%">Evgenij Thorstensen</style></author><author><style face="normal" font="default" size="100%">Johannes Trame</style></author><author><style face="normal" font="default" size="100%">Arild Waaler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">Tudor Groza</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optique 1.0: Semantic Access to Big Data: The Case of Norwegian Petroleum Directorate's FactPages</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ISWC 2013 Posters &amp; Demonstrations Track, Sydney, Australia, October 23, 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">1035</style></volume><pages><style face="normal" font="default" size="100%">65–68</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frederick Maier</style></author><author><style face="normal" font="default" size="100%">Yue Ma</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Paraconsistent OWL and Related Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automated Deduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Complexity</style></keyword><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Paraconsistency</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword><keyword><style  face="normal" font="default" size="100%">Web Ontology Language</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2012-0066</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">395–427</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Web Ontology Language OWL is currently the most prominent formalism for representing ontologies in Semantic Web applications. OWL is based on description logics, and automated reasoners are used to infer knowledge implicitly present in OWL ontologies. However, because typical description logics obey the classical principle of explosion, reasoning over inconsistent ontologies is impossible in OWL. This is so despite the fact that inconsistencies are bound to occur in many realistic cases, e.g., when multiple ontologies are merged or when ontologies are created by machine learning or data mining tools. In this paper, we present four-valued paraconsistent description logics which can reason over inconsistencies. We focus on logics corresponding to OWL DL and its profiles. We present the logic SROIQ4, showing that it is both sound relative to classical SROIQ and that its embedding into SROIQ is consequence preserving. We also examine paraconsistent varieties of EL++, DL-Lite, and Horn-DLs. The general framework described here has the distinct advantage of allowing classical reasoners to draw sound but nontrivial conclusions from even inconsistent knowledge bases. Truth-value gaps and gluts can also be selectively eliminated from models (by inserting additional axioms into knowledge bases). If gaps but not gluts are eliminated, additional classical conclusions can be drawn without affecting paraconsistency.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings of the Ninth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'13, at the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 2013</style></title><secondary-title><style face="normal" font="default" size="100%">Ninth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'13, at the 23rd International Joint Conference on Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Beijing, China</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shasha Huang</style></author><author><style face="normal" font="default" size="100%">Qingguo Li</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reasoning with Inconsistencies in Hybrid MKNF Knowledge Bases</style></title><secondary-title><style face="normal" font="default" size="100%">Logic Journal of the IGPL</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Data complexity</style></keyword><keyword><style  face="normal" font="default" size="100%">Description logics and rules</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Non-monotonic reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Paraconsistent reasoning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1093/jigpal/jzs043</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">263–290</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper is concerned with the handling of inconsistencies occurring in the combination of description logics and rules, especially in hybrid MKNF knowledge bases. More precisely, we present a paraconsistent semantics for hybrid MKNF knowledge bases (called para-MKNF knowledge bases) based on four-valued logic as proposed by Belnap. We also reduce this paraconsistent semantics to the stable model semantics via a linear transformation operator, which shows the relationship between the two semantics and indicates that the data complexity in our paradigm is not higher than that of classical reasoning. Moreover, we provide fixpoint operators to compute paraconsistent MKNF models, each suitable to different kinds of rules. At last we present the data complexity of instance checking in different paraMKNF knowledge bases.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhangquan Zhou</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author><author><style face="normal" font="default" size="100%">Chang Liu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lluis Godo</style></author><author><style face="normal" font="default" size="100%">Henri Prade</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Scale reasoning with fuzzy-EL+ ontologies based on MapReduce</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IJCAI-2013 Workshop on Weighted Logics for Artificial Intelligence (WL4AI 2013)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Beijing, China</style></pub-location><pages><style face="normal" font="default" size="100%">87-93</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Fuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy or vague knowledge. In this paper, we consider the problem of reasoning with fuzzy-EL+, which is a fuzzy extension of EL+. We first identify the challenges and present revised completion classification rules for fuzzy-EL+ that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-EL+ ontologies using MapReduce. Some preliminary experimental results are provided to show the scalability of our algorithm.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantics for Big Data: Papers from the AAAI Symposium, November 15-17, 2013, Arlington, Virginia</style></title><secondary-title><style face="normal" font="default" size="100%">AAAI Symposium on Semantics for Big Data</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Arlington, Virginia, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ebrahimi, Monireh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Side Effects Recognition as Implicit Opinion Words in Drug Reviews</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Universiti Teknologi Malaysia</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Master</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cong Wang</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SROIQ Syntax Approximation by Using Nominal Schemas</style></title><secondary-title><style face="normal" font="default" size="100%">Informal Proceedings of the 26th International Workshop on Description Logics, Ulm, Germany, July 23 - 26, 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1014/paper_31.pdf</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">988–999</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">String Similarity Metrics for Ontology Alignment</style></title><secondary-title><style face="normal" font="default" size="100%">International Semantic Web Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Sydney, Australia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div&gt;
&lt;p&gt;&amp;nbsp;&lt;/p&gt;
&lt;/div&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">StringsAuto and MapSSS Results for OAEI 2013</style></title><secondary-title><style face="normal" font="default" size="100%">8th International Workshop on Ontology Matching</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Sydney, Australia</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prateek Jain</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Chitra Venkatramani</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">There’s No Money in Linked Data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">DaSe Lab, Department of Computer Science and Engineering, Wright State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Dayton, OH, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Linked Data (LD) has been an active research area for more than 6 years and many aspects about publishing, retrieving, linking, and cleaning Linked Data have been investigated. There seems to be a broad and general agreement that in principle LD datasets can be very useful for solving a wide variety of problems ranging from practical industrial analytics to highly specific research problems. Having these notions in mind, we started exploring the use of notable LD datasets such as DBpedia, Freebase, Geonames and others for a commercial application. However, it turns out that using these datasets in realistic settings is not always easy. Surprisingly, in many cases the underlying issues are not technical but legal barriers erected by the LD data publishers. In this paper we argue that these barriers are often not justified, detrimental to both data publishers and users, and are often built without much consideration of their consequences.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Paul N. Bennett</style></author><author><style face="normal" font="default" size="100%">Evgeniy Gabrilovich</style></author><author><style face="normal" font="default" size="100%">Jaap Kamps</style></author><author><style face="normal" font="default" size="100%">Jussi Karlgren</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Thoughts on the Complex Relation Between Linked Data, Semantic Annotations, and Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">ESAIR'13, Proceedings of the Sixth International Workshop on Exploiting Semantic Annotations in Information Retrieval, co-located with {CIKM} 2013, San Francisco, CA, USA, October 28, 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/2513204.2513218</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">San Francisco, CA</style></pub-location><pages><style face="normal" font="default" size="100%">41–44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The relation between data, annotations, and schemata seems straightforward at first: Data are annotated with additional meta information according to some schemata in order to expose additional non-intrinsic characteristics relevant to the meaningful interpretation of said data. However, on closer examination, things are not as simple. Focusing on geo-information retrieval, we will try to disentangle the aforementioned relations. We will report from our own experience and from observations gathered by editing papers about ontologies and Linked Data for the Semantic Web journal.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Cong Wang</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards an Efficient Algorithm to Reason over Description Logics Extended with Nominal Schemas</style></title><secondary-title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems - 7th International Conference, {RR} 2013, Mannheim, Germany, July 27-29, 2013. Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">EL++</style></keyword><keyword><style  face="normal" font="default" size="100%">Nominal Schemas</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-39666-3_6</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">65–79</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Extending description logics with so-called nominal schemas has been shown to be a major step towards integrating description logics with rules paradigms. However, establishing efficient algorithms for reasoning with nominal schemas has so far been a challenge. In this paper, we present an algorithm to reason with the description logic fragment ELROVn, a fragment that extends EL++ with nominal schemas. We also report on an implementation and experimental evaluation of the algorithm, which shows that our approach is indeed rather efficient.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author></authors><subsidiary-authors><author><style face="normal" font="default" size="100%">Yong Yu</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author><author><style face="normal" font="default" size="100%">Haofen Wang</style></author><author><style face="normal" font="default" size="100%">Chang Liu</style></author></subsidiary-authors></contributors><titles><title><style face="normal" font="default" size="100%">语义Web技术基础</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Tsinghua University Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joshi, Amit Krishna</style></author><author><style face="normal" font="default" size="100%">Prateek Jain</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Peter Z. Yeh</style></author><author><style face="normal" font="default" size="100%">Kunal Verma</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Mariana Damova</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Alignment-based querying of linked open data</style></title><secondary-title><style face="normal" font="default" size="100%">On the Move to Meaningful Internet Systems: OTM 2012</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">807–824</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dedre Gentner</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Kai-Uwe Kühnberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221)</style></title><secondary-title><style face="normal" font="default" size="100%">Dagstuhl Reports</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.4230/DagRep.2.5.93</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">93–116</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cong Wang</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Consequence-Based Procedure for Description Logics with Self-Restriction</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web and Web Science - 6th Chinese Semantic Web Symposium and 1st Chinese Web Science Conference, CSWS 2012, Shenzhen, China, November 28-30, 2012.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-1-4614-6880-6_15</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">169–180</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Digital Earth as Knowledge Engine</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2012-0070</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">213–221</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Digital Earth aims at developing a digital representation of the planet. It is motivated by the need for integrating and interlinking vast geo-referenced, multi-thematic, and multi-perspective knowledge archives that cut through domain boundaries. Complex scientific questions cannot be answered from within one domain alone but span over multiple scientific disciplines. For instance, studying disease dynamics for prediction and policy making requires data and models from a diverse body of science ranging from medical science and epidemiology over geography and economics to mining the social Web. The naive assumption that such problems can simply be addressed by more data with a higher spatial, temporal, and thematic resolution fails as long as this more on data is not supported by more knowledge on how to combine and interpret the data. This makes semantic interoperability a core research topic of data-intensive science. While the Digital Earth vision includes processing services, it is, at its very core, a data archive and infrastructure. We propose to redefine the Digital Earth as a knowledge engine and discuss what the Semantic Web has to offer in this context and to Big Data in general.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extending Description Logic Rules</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web: Research and Applications - 9th Extended Semantic Web Conference, ESWC 2012, Heraklion, Crete, Greece, May 27-31, 2012. Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Rules</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-30284-8_30</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">345–359</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Description Logics – the logics underpinning the Web Ontology Language OWL – and rules are currently the most prominent paradigms used for modeling knowledge for the Semantic Web. While both of these approaches are based on classical logic, the paradigms also differ significantly, so that naive combinations result in undesirable properties such as undecidability. Recent work has shown that many rules can in fact be expressed in OWL. In this paper we extend this work to include some types of rules previously excluded. We formally define a set of first order logic rules, C-Rules, which can be expressed within OWL extended with role conjunction. We also show that the use of nominal schemas results in even broader coverage.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">How I Would Like Semantic Web To Be, For My Children</style></title><secondary-title><style face="normal" font="default" size="100%">What will the Semantic Web look like 10 years from now? co-located with the 11th International Semantic Web Conference 2012 (ISWC 2012)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston, MA, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Semantic Web, since its inception, has gone through lot of developments in its relatively nascent existence; right from people’s perception, to the standards and to its adoption by the industry and more importantly by the scientific community. This impressive growth only seems to increase. In this paper, we project this growth to the next 10 years and highlight some of the facets on which Semantic Web could have a major impact on. We also present the challenges that Semantic Web and its community has to deal with in order to get there.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pavel Klinov</style></author><author><style face="normal" font="default" size="100%">Matthew Horridge</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Integrating OWL and Rules: A Syntax Proposal for Nominal Schemas</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of OWL: Experiences and Directions Workshop 2012, Heraklion, Crete, Greece, May 27-28, 2012</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-849/paper_6.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">849</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper proposes an addition to OWL 2 syntax to incorporate nominal schemas, which is a new description-logic style extension of OWL 2 which was recently proposed, and which makes is possible to express “variable nominal classes” within axioms in an OWL 2 ontology. Nominal schemas make it possible to express DL-safe rules of arbitrary arity within the extended OWL paradigm, hence covering the well-known DL-safe SWRL language. To express this feature, we extend OWL 2 syntax to include necessary and minimal modifications to both Functional and Manchester syntax grammars and mappings from these two syntaxes to Turtle/RDF. We also include several examples to clarify the proposal.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Silvana Castano</style></author><author><style face="normal" font="default" size="100%">Panos Vassiliadis</style></author><author><style face="normal" font="default" size="100%">Laks V. S. Lakshmanan</style></author><author><style face="normal" font="default" size="100%">Mong-Li Lee</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Key Ingredients For Your Next Semantics Elevator Talk</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Conceptual Modeling - {ER} 2012 Workshops CMS, ECDM-NoCoDA, MoDIC, MORE-BI, RIGiM, SeCoGIS, WISM, Florence, Italy, October 15-18, 2012. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-33999-8_27</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Florence, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">7518</style></volume><pages><style face="normal" font="default" size="100%">213–220</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;2012 brought a major change to the semantics research community. Discussions on the use and benefits of semantic technologies are shifting away from the why to the how. Surprisingly this more in stakeholder interest is not accompanied by a more detailed understanding of what semantics research is about. Instead of blaming others for their (wrong) expectations, we need to learn how to emphasize the paradigm shift proposed by semantics research while abstracting from technical details and advocate the added value in a way that relates to the immediate needs of individual stakeholders without overselling. This paper highlights some of the major ingredients to prepare your next Semantics Elevator Talk.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Joshi, Amit Krishna</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Cong Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Konf Connect</style></title><secondary-title><style face="normal" font="default" size="100%">Metadata Challenge at the 21st International Conference on World Wide Web (WWW 2012)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><pub-location><style face="normal" font="default" size="100%">Lyon, France</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present an application called Konf-Connect to improve&amp;nbsp;the conference attending experience of the people who attend a conference. This tool provides search facilities to nd people with similar&amp;nbsp;interests. The application makes use of Semantic Web dog food dataset&amp;nbsp;to gather information regarding the conference at hand. This is helpful for people attending the conference who are looking for networking&amp;nbsp;opportunities with people having expertise in the specic areas of interest. The application can also be extended to be used as general purpose&amp;nbsp;expert search system.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A logical geo-ontology design pattern for quantifying over types</style></title><secondary-title><style face="normal" font="default" size="100%">SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems (formerly known as GIS), SIGSPATIAL'12, Redondo Beach, CA, USA, November 7-9, 2012</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biodiversity</style></keyword><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Design Patterns</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/2424321.2424352</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">239–248</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prateek Jain</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Kunal Verma</style></author><author><style face="normal" font="default" size="100%">Peter Z. Yeh</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ethan V. Munson</style></author><author><style face="normal" font="default" size="100%">Markus Strohmaier</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Moving beyond SameAs with PLATO: Partonomy detection for Linked Data</style></title><secondary-title><style face="normal" font="default" size="100%">23rd ACM Conference on Hypertext and Social Media, HT '12</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Linked Open Data Cloud</style></keyword><keyword><style  face="normal" font="default" size="100%">Mereology</style></keyword><keyword><style  face="normal" font="default" size="100%">Part of Relation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/2309996.2310004</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pub-location><style face="normal" font="default" size="100%">Milwaukee, WI, USA</style></pub-location><pages><style face="normal" font="default" size="100%">33–42</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery.&lt;/p&gt;

&lt;p&gt;Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications.&lt;/p&gt;

&lt;p&gt;In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Open and transparent: the review process of the Semantic Web journal</style></title><secondary-title><style face="normal" font="default" size="100%">Learned Publishing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">48-55</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;While open access is established in the world of academic publishing, open reviews are rare. The Semantic Web journal goes further than just open review by implementing an open and transparent review process in which reviews are publicly available, the assigned editors and reviewers are known by name, and are published together with accepted manuscripts. In this article we introduce the steps to realize such a process from the conceptual design, over the implementation, a overview of the results so far, and up to lessons learned.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>12</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Bijan Parsia</style></author><author><style face="normal" font="default" size="100%">Peter F. Patel-Schneider</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">OWL 2 Web Ontology Language: Primer (Second Edition)</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/11/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.w3.org/TR/owl2-primer</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">W3C Recommendation</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings of the Eighth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'12, at the 26th Conference on Artificial Intelligence, AAAI-12, Toronto, Canada, July 2012</style></title><secondary-title><style face="normal" font="default" size="100%">Eighth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'12, at the 26th Conference on Artificial Intelligence, AAAI-12</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><pub-location><style face="normal" font="default" size="100%">Toronto, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cong Wang</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reasoning Approaches for Nominal Schemas</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">JIST</style></publisher><pub-location><style face="normal" font="default" size="100%">Nara, Japan</style></pub-location><volume><style face="normal" font="default" size="100%">Poster and Demonstration Proceedings</style></volume></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhangquan Zhou</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author><author><style face="normal" font="default" size="100%">Chang Liu</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Luc De Raedt</style></author><author><style face="normal" font="default" size="100%">Christian Bessière</style></author><author><style face="normal" font="default" size="100%">Didier Dubois</style></author><author><style face="normal" font="default" size="100%">Patrick Doherty</style></author><author><style face="normal" font="default" size="100%">Paolo Frasconi</style></author><author><style face="normal" font="default" size="100%">Fredrik Heintz</style></author><author><style face="normal" font="default" size="100%">Peter J. F. Lucas</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Reasoning with Fuzzy-EL+ Ontologies Using MapReduce</style></title><secondary-title><style face="normal" font="default" size="100%">ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/978-1-61499-098-7-933</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Montpellier, France</style></pub-location><volume><style face="normal" font="default" size="100%">242</style></volume><pages><style face="normal" font="default" size="100%">933–934</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Fuzzy extension of Description Logics (DLs) allows the formal representation and handling of fuzzy knowledge. In this paper, we consider fuzzy-EL+, which is a fuzzy extension of EL+. We first present revised completion rules for fuzzy-EL+ that can be handled by MapReduce programs. We then propose an algorithm for scale reasoning with fuzzy-EL+ ontologies based on MapReduce.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Matthias Knorr</style></author><author><style face="normal" font="default" size="100%">David Carral</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Frederick Maier</style></author><author><style face="normal" font="default" size="100%">Cong Wang</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Umberto Straccia</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Recent Advances in Integrating OWL and Rules</style></title><secondary-title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems - 6th International Conference, RR 2012, Vienna, Austria, September 10-12, 2012. Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Rules</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-33203-6_20</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Austria, Vienna</style></pub-location><volume><style face="normal" font="default" size="100%">7497</style></volume><pages><style face="normal" font="default" size="100%">225-228</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">As part of the quest for a unifying logic for the Semantic Web Technology Stack, a central issue is finding suitable ways of integrating description logics based on the Web Ontology Language (OWL) with rule-based approaches based on logic programming. Such integration is difficult since naive approaches typically result in the violation of one or more desirable design principles. For example, while both OWL 2 DL and RIF Core (a dialect of the Rule Interchange Format RIF) are decidable, their naive union is not, unless carefully chosen syntactic restrictions are applied.

We report on recent advances and ongoing work by the authors in integrating OWL and rulesWe take an OWL-centric perspective, which means that we take OWL 2 DL as a starting point and pursue the question of how features of rulebased formalisms can be added without jeopardizing decidability. We also report on incorporating the closed world assumption and on reasoning algorithms. This paper essentially serves as an entry point to the original papers, to which we will refer throughout, where detailed expositions of the results can be found.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Matthias Knorr</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Frederick Maier</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Luc De Raedt</style></author><author><style face="normal" font="default" size="100%">Christian Bessière</style></author><author><style face="normal" font="default" size="100%">Didier Dubois</style></author><author><style face="normal" font="default" size="100%">Patrick Doherty</style></author><author><style face="normal" font="default" size="100%">Paolo Frasconi</style></author><author><style face="normal" font="default" size="100%">Fredrik Heintz</style></author><author><style face="normal" font="default" size="100%">Peter J. F. Lucas</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Reconciling OWL and Non-monotonic Rules for the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/978-1-61499-098-7-474</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IOS Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Montpellier, France</style></pub-location><volume><style face="normal" font="default" size="100%">242</style></volume><pages><style face="normal" font="default" size="100%">474–479</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We propose a description logic extending SROIQ (the description logic underlying OWL 2 DL) and at the same time encompassing some of the most prominent monotonic and nonmonotonic rule languages, in particular Datalog extended with the answer set semantics. Our proposal could be considered a substantial contribution towards fulfilling the quest for a unifying logic for the Semantic Web. As a case in point, two non-monotonic extensions of description logics considered to be of distinct expressiveness until now are covered in our proposal. In contrast to earlier such proposals, our language has the “look and feel” of a description logic and avoids hybrid or first-order syntaxes.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vikas Agrawal</style></author><author><style face="normal" font="default" size="100%">Jorge Baier</style></author><author><style face="normal" font="default" size="100%">Kostas E. Bekris</style></author><author><style face="normal" font="default" size="100%">Yiling Chen</style></author><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Patrik Haslum</style></author><author><style face="normal" font="default" size="100%">Dietmar Jannach</style></author><author><style face="normal" font="default" size="100%">Edith Law</style></author><author><style face="normal" font="default" size="100%">Freddy Lécué</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author><author><style face="normal" font="default" size="100%">Cynthia Matuszek</style></author><author><style face="normal" font="default" size="100%">Héctor Palacios</style></author><author><style face="normal" font="default" size="100%">Biplav Srivastava</style></author><author><style face="normal" font="default" size="100%">Lokendra Shastri</style></author><author><style face="normal" font="default" size="100%">Nathan R. Sturtevant</style></author><author><style face="normal" font="default" size="100%">Roni Stern</style></author><author><style face="normal" font="default" size="100%">Stefanie Tellex</style></author><author><style face="normal" font="default" size="100%">Stavros Vassos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reports of the AAAI 2012 Conference Workshops</style></title><secondary-title><style face="normal" font="default" size="100%">AI Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ojs/index.php/aimagazine/article/view/2444</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">119–127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vikas Agrawal</style></author><author><style face="normal" font="default" size="100%">Jorge Baier</style></author><author><style face="normal" font="default" size="100%">Kostas E. Bekris</style></author><author><style face="normal" font="default" size="100%">Yiling Chen</style></author><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Patrik Haslum</style></author><author><style face="normal" font="default" size="100%">Dietmar Jannach</style></author><author><style face="normal" font="default" size="100%">Edith Law</style></author><author><style face="normal" font="default" size="100%">Freddy Lécué</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author><author><style face="normal" font="default" size="100%">Cynthia Matuszek</style></author><author><style face="normal" font="default" size="100%">Héctor Palacios</style></author><author><style face="normal" font="default" size="100%">Biplav Srivastava</style></author><author><style face="normal" font="default" size="100%">Lokendra Shastri</style></author><author><style face="normal" font="default" size="100%">Nathan R. Sturtevant</style></author><author><style face="normal" font="default" size="100%">Roni Stern</style></author><author><style face="normal" font="default" size="100%">Stefanie Tellex</style></author><author><style face="normal" font="default" size="100%">Stavros Vassos</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reports of the AAAI 2012 Conference Workshops</style></title><secondary-title><style face="normal" font="default" size="100%">AI Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ojs/index.php/aimagazine/article/view/2444</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">119-127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cong Wang</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Resolution Procedure for Description Logics with Nominal Schemas</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Technology, Second Joint International Conference, JIST 2012, Nara, Japan, December 2-4, 2012. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-37996-3_1</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1–16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Gary Berg-Cross</style></author><author><style face="normal" font="default" size="100%">Leo Obrst</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Timothy Finin</style></author><author><style face="normal" font="default" size="100%">Isabel Cruz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Aspects of EarthCube</style></title><secondary-title><style face="normal" font="default" size="100%">EarthCube report of the Technology Subcommittee of the EarthCube Semantics and Ontologies Group</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this document, we give a high-level overview of selected Semantic (Web) technologies, methods, and other important considerations, that are relevant for the success of EarthCube. The goal of this initial document is to provide entry points and references for discussions between the Semantic Technologies experts and the domain experts within EarthCube. The selected topics are intended to ground the EarthCube roadmap in the state of the art in semantics research and ontology engineering.&lt;/p&gt;

&lt;p&gt;We anticipate that this document will evolve as EarthCube progresses. Indeed, all EarthCube parties are asked to provide topics of importance that should be treated in future versions of this document.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gary Berg-Cross</style></author><author><style face="normal" font="default" size="100%">Isabel Cruz</style></author><author><style face="normal" font="default" size="100%">Mike Dean</style></author><author><style face="normal" font="default" size="100%">Tim Finin</style></author><author><style face="normal" font="default" size="100%">Mark Gahegan</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Hook Hua</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Naicong Li</style></author><author><style face="normal" font="default" size="100%">Philip Murphy</style></author><author><style face="normal" font="default" size="100%">Bryce Nordgren</style></author><author><style face="normal" font="default" size="100%">Leo Obrst</style></author><author><style face="normal" font="default" size="100%">Mark Schildhauer</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Krishna Sinha</style></author><author><style face="normal" font="default" size="100%">Anne Thessen</style></author><author><style face="normal" font="default" size="100%">Nancy Wiegand</style></author><author><style face="normal" font="default" size="100%">Ilya Zaslavsky</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">C. Kessler</style></author><author><style face="normal" font="default" size="100%">T. Kauppinen</style></author><author><style face="normal" font="default" size="100%">Dave Kolas</style></author><author><style face="normal" font="default" size="100%">Simon Scheider</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantics and Ontologies for EarthCube</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on GIScience in the Big Data Age, In conjunction with the seventh International Conference on Geographic Information Science 2012 (GIScience 2012)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><pub-location><style face="normal" font="default" size="100%">Columbus, Ohio, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Semantic technologies and ontologies play an increasing role in scientific workflow systems and knowledge infrastructures. While ontologies are mostly used for the semantic annotation of metadata, semantic technologies enable searching metadata catalogs beyond simple keywords, with some early evidence of semantics used for data translation. However, the next generation of distributed and interdisciplinary knowledge infrastructures will require capabilities beyond simple subsumption reasoning over subclass relations. In this work, we report from the EarthCube Semantics Community by highlighting which role semantics and ontologies should play in the EarthCube knowledge infrastructure. We target the interested domain scientist and, thus, introduce the value proposition of semantic technologies in a non-technical language. Finally, we commit ourselves to some guiding principles for the successful implementation and application of semantic technologies and ontologies within EarthCube.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Umberto Straccia</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Tableau Algorithm for Description Logics with Nominal Schemas</style></title><secondary-title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems, 6th International Conference, RR2012, Vienna, Austria, September 10-12, 2012, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2012</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">7497</style></volume><pages><style face="normal" font="default" size="100%">234-237</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We present a tableau algorithm for the description logic&amp;nbsp;ALCOV. This description logic is obtained by extending the description&amp;nbsp;logic ALCO with the expressive nominal schema construct that enables&amp;nbsp;DL-safe datalog with predicates of arbitrary arity to be covered within&amp;nbsp;the description logic framework. The tableau algorithm provides a basis to implement a delayed grounding strategy which was not facilitated&amp;nbsp;by earlier versions of decision procedures for satisfiability in expressive&amp;nbsp;description logics with nominal schemas.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Joshi, Amit Krishna</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Dong, Guozhu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards logical linked data compression</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Joint Workshop on Large and Heterogeneous Data and Quantitative Formalization in the Semantic Web, LHD+ SemQuant2012, at the 11th International Semantic Web Conference, ISWC2012</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Type-Elimination-Based Reasoning for the Description Logic SHIQbs using Decision Diagrams and Disjunctive Datalog</style></title><secondary-title><style face="normal" font="default" size="100%">Logical Methods in Computer Science</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">datalog</style></keyword><keyword><style  face="normal" font="default" size="100%">decision diagrams</style></keyword><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">type elimination</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.2168/LMCS-8(1:12)2012</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a novel, type-elimination-based method for standard reasoning in the description logic SHIQbs extended by DL-safe rules. To this end, we first establish a knowledge compilation method converting the terminological part of an ALCIb knowledge base into an ordered binary decision diagram (OBDD) that represents a canonical model. This OBDD can in turn be transformed into disjunctive Datalog and merged with the assertional part of the knowledge base in order to perform combined reasoning. In order to leverage our technique for full SHIQbs, we provide a stepwise reduction from SHIQbs to ALCIb that preserves satisfiability and entailment of positive and negative ground facts. The proposed technique is shown to be worst-case optimal w.r.t. combined and data complexity.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Philippe Cudré-Mauroux</style></author><author><style face="normal" font="default" size="100%">Jeff Heflin</style></author><author><style face="normal" font="default" size="100%">Evren Sirin</style></author><author><style face="normal" font="default" size="100%">Tania Tudorache</style></author><author><style face="normal" font="default" size="100%">Jérôme Euzenat</style></author><author><style face="normal" font="default" size="100%">Manfred Hauswirth</style></author><author><style face="normal" font="default" size="100%">Josiane Xavier Parreira</style></author><author><style face="normal" font="default" size="100%">James A. Hendler</style></author><author><style face="normal" font="default" size="100%">Guus Schreiber</style></author><author><style face="normal" font="default" size="100%">Abraham Bernstein</style></author><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Very Large Scale OWL Reasoning through Distributed Computation</style></title><secondary-title><style face="normal" font="default" size="100%">11th International Semantic Web Conference (ISWC 2012), Proceedings, Part II</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Distributed Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontology Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL EL</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-35173-0_30</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston, MA, USA</style></pub-location><volume><style face="normal" font="default" size="100%">7650</style></volume><pages><style face="normal" font="default" size="100%">407–414</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Due to recent developments in reasoning algorithms of the&amp;nbsp;various OWL profiles, the classification time for an ontology has come&amp;nbsp;down drastically. For all of the popular reasoners, in order to process&amp;nbsp;an ontology, an implicit assumption is that the ontology should fit in&amp;nbsp;primary memory. The memory requirements for a reasoner are already&amp;nbsp;quite high, and considering the ever increasing size of the data to be&amp;nbsp;processed and the goal of making reasoning Web scale, this assumption&amp;nbsp;becomes overly restrictive. In our work, we study several distributed&amp;nbsp;classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Frederick Maier</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Sadagopan Srinivasan</style></author><author><style face="normal" font="default" size="100%">Krithi Ramamritham</style></author><author><style face="normal" font="default" size="100%">Arun Kumar</style></author><author><style face="normal" font="default" size="100%">M. P. Ravindra</style></author><author><style face="normal" font="default" size="100%">Elisa Bertino</style></author><author><style face="normal" font="default" size="100%">Ravi Kumar</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Better Uncle for OWL: Nominal Schemas for Integrating Rules and Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, March 28 - April 1, 2011</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">datalog</style></keyword><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web Rule Language</style></keyword><keyword><style  face="normal" font="default" size="100%">SROIQ</style></keyword><keyword><style  face="normal" font="default" size="100%">tractability</style></keyword><keyword><style  face="normal" font="default" size="100%">Web Ontology Language</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/1963405.1963496</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">645-654</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-0632-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a description-logic style extension of OWL 2 with nominal schemas which can be used like &quot;variable nominal classes&quot; within axioms. This feature allows ontology languages to express arbitrary DL-safe rules (as expressible in SWRL or RIF) in their native syntax. We show that adding nominal schemas to OWL 2 does not increase the worst-case reasoning complexity, and we identify a novel tractable language SROELV3(\cap, x) that is versatile enough to capture the lightweight languages OWL EL and OWL RL.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yue Ma</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computing Inconsistency Measure based on Paraconsistent Semantics</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Logic and Computation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1093/logcom/exq053</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">1257–1281</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Measuring inconsistency in knowledge bases has been recognized as an important problem in several research areas. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. However, existing methods suffer from two limitations: 1) They are mostly restricted to propositional knowledge bases; 2) Very few of them discuss computational aspects of computing inconsistency measures. In this paper, we try to solve these two limitations by exploring algorithms for computing an inconsistency measure of first-order knowledge bases. After introducing a four-valued semantics for first-order logic, we define an inconsistency measure of a first-order knowledge base, which is a sequence of inconsistency degrees. We then propose a precise algorithm to compute our inconsistency measure. We show that this algorithm reduces the computation of the inconsistency measure to classical satisfiability checking. This is done by introducing a new semantics, named S[n]-4 semantics, which can be calculated by invoking a classical SAT solver. Moreover, we show that this auxiliary semantics also gives a direct way to compute upper and lower bounds of inconsistency degrees. That is, it can be easily revised to compute approximating inconsistency measures. The approximating inconsistency measures converge to the precise values if enough resources are available. Finally, by some nice properties of the S[n]-4 semantics, we show that some upper and lower bounds can be computed in P-time, which says that the problem of computing these approximating inconsistency measures is tractable.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prateek Jain</style></author><author><style face="normal" font="default" size="100%">Peter Z. Yeh</style></author><author><style face="normal" font="default" size="100%">Kunal Verma</style></author><author><style face="normal" font="default" size="100%">Reymonrod G. Vasquez</style></author><author><style face="normal" font="default" size="100%">Mariana Damova</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Grigoris Antoniou</style></author><author><style face="normal" font="default" size="100%">Marko Grobelnik</style></author><author><style face="normal" font="default" size="100%">Elena Paslaru Bontas Simperl</style></author><author><style face="normal" font="default" size="100%">Bijan Parsia</style></author><author><style face="normal" font="default" size="100%">Dimitris Plexousakis</style></author><author><style face="normal" font="default" size="100%">Pieter De Leenheer</style></author><author><style face="normal" font="default" size="100%">Jeff Z. Pan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web: Research and Applications - 8th Extended Semantic Web Conference, ESWC 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-21034-1_6</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heraklion, Crete, Greece</style></pub-location><volume><style face="normal" font="default" size="100%">6643</style></volume><pages><style face="normal" font="default" size="100%">80–92</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;The Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies – in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a more sophisticated metric to determine which classes between two ontologies to align, and 2) considers contextual information to further support (or reject) an alignment. We present a comprehensive evaluation of our solution using schema-level mappings from LOD ontologies to Proton (an upper level ontology) – created manually by human experts for a real world application called FactForge. We show that our solution performed well on this task. We also show that our solution significantly outperformed existing ontology alignment solutions (including our previously published work on BLOOMS) on this same task.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Matthias Knorr</style></author><author><style face="normal" font="default" size="100%">José Júlio Alferes</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Local Closed World Reasoning with Description Logics under the Well-Founded Semantics</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Logic Programming</style></keyword><keyword><style  face="normal" font="default" size="100%">Non-monotonic reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontologies</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1016/j.artint.2011.01.007</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">175</style></volume><pages><style face="normal" font="default" size="100%">1528–1554</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases [52], and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9-10</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author><author><style face="normal" font="default" size="100%">Claudio Gutierrez</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Local Closed World Semantics: Grounded Circumscription for Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems - 5th International Conference, RR 2011, Galway, Ireland, August 29-30, 2011. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-23580-1</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6902</style></volume><pages><style face="normal" font="default" size="100%">263-268</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-23579-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present an improved local closed world extension for description logics. It is based on circumscription, and deviates from previous circumscriptive description logics in that extensions of minimized predicates may contain only extensions of named individuals in the knowledge base. Besides an (arguably) higher intuitive appeal, the improved semantics is applicable to expressive description logics without loss of decidability.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lora Aroyo</style></author><author><style face="normal" font="default" size="100%">Chris Welty</style></author><author><style face="normal" font="default" size="100%">Harith Alani</style></author><author><style face="normal" font="default" size="100%">Jamie Taylor</style></author><author><style face="normal" font="default" size="100%">Abraham Bernstein</style></author><author><style face="normal" font="default" size="100%">Lalana Kagal</style></author><author><style face="normal" font="default" size="100%">Natasha F. Noy</style></author><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Local Closed World Semantics: Grounded Circumscription for OWL</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web - ISWC 2011 - 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2011</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">7031</style></volume><pages><style face="normal" font="default" size="100%">617-632</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new approach to adding closed world reasoning to the Web Ontology Language OWL. It transcends previous work on circumscriptive description logics which had the drawback of yielding an undecidable logic unless severe restrictions were imposed. In particular, it was not possible, in general, to apply local closure to roles. In this paper, we provide a new approach, called grounded circumscription, which is applicable to SROIQ and other description logics around OWL without these restrictions. We show that the resulting language is decidable, and we derive an upper complexity bound. We also provide a decision procedure in the form of a tableaux algorithm.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Kunal Sengupta</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Riccardo Rosati</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author><author><style face="normal" font="default" size="100%">Michael Zakharyaschev</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Local Closed World Semantics: Keep it simple, stupid!</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 24th International Workshop on Description Logics (DL 2011), Barcelona, Spain, July 13-16, 2011</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">circumscription</style></keyword><keyword><style  face="normal" font="default" size="100%">closed world</style></keyword><keyword><style  face="normal" font="default" size="100%">decidability</style></keyword><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-745/paper_12.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">745</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A combination of open and closed-world reasoning (usually called local closed world reasoning) is a desirable capability of knowledge representation formalisms for Semantic Web applications. However, none of the proposals made to date for extending description logics with local closed world capabilities has had any significant impact on applications. We believe that one of the key reasons for this is that current proposals fail to provide approaches which are intuitively accessible for application developers and at the same time are applicable, as extensions, to expressive description logics such as SROIQ, which underlies the Web Ontology Language OWL. In this paper we propose a new approach which overcomes key limitations of other major proposals made to date. It is based on an adaptation of circumscriptive description logics which, in contrast to previously reported circumscription proposals, is applicable to SROIQ without rendering reasoning over the resulting language undecidable.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steffen Hölldobler</style></author><author><style face="normal" font="default" size="100%">Sebastian Bader</style></author><author><style face="normal" font="default" size="100%">Bertram Fronhöfer</style></author><author><style face="normal" font="default" size="100%">Ursula Hans</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Tobias Pietzsch</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Logik und Logikprogrammierung Band 2: Aufgaben und Lösungen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Synchron Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MapSSS Results for OAEI 2011</style></title><secondary-title><style face="normal" font="default" size="100%">6th International Workshop on Ontology Matching</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pub-location><style face="normal" font="default" size="100%">Bonn, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Robert Meersman</style></author><author><style face="normal" font="default" size="100%">Tharam S. Dillon</style></author><author><style face="normal" font="default" size="100%">Pilar Herrero</style></author><author><style face="normal" font="default" size="100%">Akhil Kumar</style></author><author><style face="normal" font="default" size="100%">Manfred Reichert</style></author><author><style face="normal" font="default" size="100%">Li Qing</style></author><author><style face="normal" font="default" size="100%">Beng Chin Ooi</style></author><author><style face="normal" font="default" size="100%">Ernesto Damiani</style></author><author><style face="normal" font="default" size="100%">Douglas C. Schmidt</style></author><author><style face="normal" font="default" size="100%">Jules White</style></author><author><style face="normal" font="default" size="100%">Manfred Hauswirth</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Mukesh K. Mohania</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Move to Meaningful Internet Systems: OTM 2011. Confederated International Conferences, CoopIS, DOA-SVI, and ODBASE 2011, Hersonissos, Crete, Greece, October 17-21, 2011, Proceedings, Part I</style></title><secondary-title><style face="normal" font="default" size="100%">Confederated International Conferences, CoopIS, DOA-SVI, and ODBASE 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Hersonissos, Crete, Greece</style></pub-location><volume><style face="normal" font="default" size="100%">7044</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Robert Meersman</style></author><author><style face="normal" font="default" size="100%">Tharam S. Dillon</style></author><author><style face="normal" font="default" size="100%">Pilar Herrero</style></author><author><style face="normal" font="default" size="100%">Akhil Kumar</style></author><author><style face="normal" font="default" size="100%">Manfred Reichert</style></author><author><style face="normal" font="default" size="100%">Li Qing</style></author><author><style face="normal" font="default" size="100%">Beng Chin Ooi</style></author><author><style face="normal" font="default" size="100%">Ernesto Damiani</style></author><author><style face="normal" font="default" size="100%">Douglas C. Schmidt</style></author><author><style face="normal" font="default" size="100%">Jules White</style></author><author><style face="normal" font="default" size="100%">Manfred Hauswirth</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Mukesh K. Mohania</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Move to Meaningful Internet Systems: OTM 2011. Confederated International Conferences, CoopIS, DOA-SVI, and ODBASE 2011, Hersonissos, Crete, Greece, October 17-21, 2011, Proceedings, Part II</style></title><secondary-title><style face="normal" font="default" size="100%">Confederated International Conferences, CoopIS, DOA-SVI, and ODBASE 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Hersonissos, Crete, Greece</style></pub-location><volume><style face="normal" font="default" size="100%">7045</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Frederick Maier</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Riccardo Rosati</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author><author><style face="normal" font="default" size="100%">Michael Zakharyaschev</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Nominal Schemas for Integrating Rules and Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 24th International Workshop on Description Logics (DL 2011), Barcelona, Spain, July 13-16, 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-745/paper_39.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">745</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose an extension of SROIQ with nominal schemas which can be used like “variable nominal concepts” within axioms. This feature allows us to express arbitrary DL-safe rules in description logic
syntax. We show that adding nominal schemas to SROIQ does not increase its worst-case reasoning complexity, and we identify a family of tractable DLs SROELVn that allow for restricted use of nominal
schemas.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Frederick Maier</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Axel Polleres</style></author><author><style face="normal" font="default" size="100%">Claudia d'Amato</style></author><author><style face="normal" font="default" size="100%">Marcelo Arenas</style></author><author><style face="normal" font="default" size="100%">Siegfried Handschuh</style></author><author><style face="normal" font="default" size="100%">Paula Kroner</style></author><author><style face="normal" font="default" size="100%">Sascha Ossowski</style></author><author><style face="normal" font="default" size="100%">Peter F. Patel-Schneider</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">OWL and Rules</style></title><secondary-title><style face="normal" font="default" size="100%">Reasoning Web. Semantic Technologies for the Web of Data - 7th International Summer School 2011, Galway, Ireland, August 23-27, 2011, Tutorial Lectures</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-23032-5</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6848</style></volume><pages><style face="normal" font="default" size="100%">382-415</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-23031-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The relationship between the Web Ontology Language OWL and rule-based formalisms has been the subject of many discussions and research investigations, some of them controversial. From the many attempts to reconcile the two paradigms, we present some of the newest developments. More precisely, we show which kind of rules can be modeled in the current version of OWL, and we show how OWL can be extended to incorporate rules. We finally give references to a large body of work on rules and OWL.
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shasha Huang</style></author><author><style face="normal" font="default" size="100%">Qingguo Li</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author><author><style face="normal" font="default" size="100%">Claudio Gutierrez</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Paraconsistent Semantics for Hybrid MKNF Knowledge Bases</style></title><secondary-title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems - 5th International Conference, RR 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-23580-1_8</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Galway, Ireland</style></pub-location><volume><style face="normal" font="default" size="100%">6902</style></volume><pages><style face="normal" font="default" size="100%">93–107</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Hybrid MKNF knowledge bases, originally based on the stable model semantics, is a mature method of combining rules and Description Logics (DLs). The well-founded semantics for such knowledge bases has been proposed subsequently for better efficiency of reasoning. However, integration of rules and DLs may give rise to inconsistencies, even if they are respectively consistent. Accordingly, reasoning systems based on the previous two semantics will break down. In this paper, we employ the four-valued logic proposed by Belnap, and present a paraconsistent semantics for Hybrid MKNF knowledge bases, which can detect inconsistencies and handle it effectively. Besides, we transform our proposed semantics to the stable model semantics via a linear transformation operator, which indicates that the data complexity in our paradigm is not higher than that of classical reasoning. Moreover, we provide a fixpoint algorithm for computing paraconsistent MKNF models.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings of the Seventh International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'11, at the 22nd International Joint Conference on Artificial Intelligence, IJCAI-11, Barcelona, Catalonia (Spain), 2011</style></title><secondary-title><style face="normal" font="default" size="100%">Seventh International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'11, at the 22nd International Joint Conference on Artificial Intelligence, IJCAI-11</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-764</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Barcelona, Catalonia, Spain</style></pub-location><volume><style face="normal" font="default" size="100%">764</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cory A. Henson</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author><author><style face="normal" font="default" size="100%">Amit P. Sheth</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Michel Dumontier</style></author><author><style face="normal" font="default" size="100%">Mélanie Courtot</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Representation of Parsimonious Covering Theory in OWL-DL</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 8th International Workshop on OWL: Experiences and Directions {(OWLED} 2011), San Francisco, California, USA, June 5-6, 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">San Francisco, California, USA</style></pub-location><volume><style face="normal" font="default" size="100%">796</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Web Ontology Language has not been designed for representing abductive inference, which is often required for applications such as medical disease diagnosis. As a consequence, existing OWL ontologies have limited ability to encode knowledge for such applications. In the last 150 years, many logic frameworks for the representation of abductive inference have been developed. Among these frameworks, Parsimonious Covering Theory (PCT) has achieved wide recognition. PCT is a formal model of diagnostic reasoning in which knowledge is represented as a network of causal associations, and whose goal is to account for observed symptoms with plausible explanatory hypotheses. In this paper, we argue that OWL does provide some of the expressivity required to approximate diagnostic reasoning, and outline a suitable encoding of PCT in OWL-DL.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Web surveys and applications</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2011-0047</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">65–66</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Web tools and systems</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2011-0035</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">1–2</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Francesco Calimeri</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems: Five Years into the Conference</style></title><secondary-title><style face="normal" font="default" size="100%">ALP Newsletter, Association of Logic Programming</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.cs.nmsu.edu/ALP/2011/12/web-reasoning-and-rule-systems-five-years-into-the-conference</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this note we retrospect on the five years of the Web Reasoning and Rule Systems conference series and discuss the rationale for the series in the context of the overall field of the Semantic Web, the activities of the Web Reasoning research community, and the development of standards for rule-based systems on the Web. At the end, we draw the reader’s attention to the next event in the series, which will take place in Vienna in September 2012.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Petko Valtchev</style></author><author><style face="normal" font="default" size="100%">Robert Jäschke</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">What's Happening in Semantic Web - ... and What FCA Could Have to Do with It</style></title><secondary-title><style face="normal" font="default" size="100%">Formal Concept Analysis - 9th International Conference, ICFCA 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-20514-9_2</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Nicosia, Cyprus</style></pub-location><volume><style face="normal" font="default" size="100%">6628</style></volume><pages><style face="normal" font="default" size="100%">18–23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Wolfgang Maass</style></author><author><style face="normal" font="default" size="100%">Marc Toussaint</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">10302 Abstracts Collection - Learning paradigms in dynamic environments</style></title><secondary-title><style face="normal" font="default" size="100%">Learning paradigms in dynamic environments</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autonomous learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural-symbolic integration</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurobiology</style></keyword><keyword><style  face="normal" font="default" size="100%">Recurrent neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Speech processing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://drops.dagstuhl.de/opus/volltexte/2010/2804</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany</style></publisher><pub-location><style face="normal" font="default" size="100%">Dagstuhl, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Wolfgang Maass</style></author><author><style face="normal" font="default" size="100%">Marc Toussaint</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Wolfgang Maass</style></author><author><style face="normal" font="default" size="100%">Marc Toussaint</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">10302 Summary – Learning paradigms in dynamic environments</style></title><secondary-title><style face="normal" font="default" size="100%">Learning paradigms in dynamic environments</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://drops.dagstuhl.de/opus/volltexte/2010/2802</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">10302</style></number><publisher><style face="normal" font="default" size="100%">Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany</style></publisher><pub-location><style face="normal" font="default" size="100%">Dagstuhl, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The seminar centered around problems which arise in the context of machine learning in dynamic environments. Particular emphasis was put on a couple of specific questions in this context: how to represent and abstract knowledge appropriately to shape the problem of learning in a partially unknown and complex environment and how to combine statistical inference and abstract symbolic representations; how to infer from few data and how to deal with non i.i.d. data, model revision and life-long learning; how to come up with efficient strategies to control realistic environments for which exploration is costly, the dimensionality is high and data are sparse; how to deal with very large settings; and how to apply these models in challenging application areas such as robotics, computer vision, or the web.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tuvshintur Tserendorj</style></author><author><style face="normal" font="default" size="100%">Stephan Grimm</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pablo Garcia Bringas</style></author><author><style face="normal" font="default" size="100%">Abdelkader Hameurlain</style></author><author><style face="normal" font="default" size="100%">Gerald Quirchmayr</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Approximate Instance Retrieval on Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">Database and Expert Systems Applications, 21st International Conference, DEXA 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-15364-8_43</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Bilbao, Spain</style></pub-location><volume><style face="normal" font="default" size="100%">6261</style></volume><pages><style face="normal" font="default" size="100%">503–511</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;With the development of more expressive description logics (DLs) for the Web Ontology Language OWL the question arises how we can properly deal with the high computational complexity for effi- cient reasoning. In application cases that require scalable reasoning with expressive ontologies, non-standard reasoning solutions such as approximate reasoning are necessary to tackle the intractability of reasoning in expressive DLs. In this paper, we are concerned with the approximation of the reasoning task of instance retrieval on DL knowledge bases, trading correctness of retrieval results for gain of speed. We introduce our notion of an approximate concept extension and we provide implementations to compute an approximate answer for a concept query by a suitable mapping to efficient database operations. Furthermore, we report on experiments of our approach on instance retrieval with the Wine ontology and discuss first results in terms of error rate and speed-up.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yue Ma</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author><author><style face="normal" font="default" size="100%">Guohui Xiao</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Zuoquan Lin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational Complexity and Anytime Algorithm for Inconsistency Measurement</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Software and Informatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">computational complexity</style></keyword><keyword><style  face="normal" font="default" size="100%">inconsistency measurement</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">multi-valued logic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41&amp;flag=1</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">3–21</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Measuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first give a complete analysis of the computational complexity of computing inconsistency degrees. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximations of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jens Lehmann</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Concept learning in description logics using refinement operators</style></title><secondary-title><style face="normal" font="default" size="100%">Machine Learning</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">Inductive logic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">refinement operators</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword><keyword><style  face="normal" font="default" size="100%">Structured Machine Learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://springerlink.metapress.com/content/c040n45u15qrnu44/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">203–250</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yue Ma</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Volker Haarslev</style></author><author><style face="normal" font="default" size="100%">David Toman</style></author><author><style face="normal" font="default" size="100%">Grant Weddell</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Distance-based Measures of Inconsistency and Incoherency for Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 23rd International Workshop on Description Logics (DL2010)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Waterloo, Canada</style></pub-location><volume><style face="normal" font="default" size="100%">573</style></volume><pages><style face="normal" font="default" size="100%">475-485</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Inconsistency and incoherency are two sorts of erroneous information in a DL ontology which have been widely discussed in ontology-based applications. For example, they have been used to detect modeling errors during ontology construction. To provide more informative metrics which can tell the differences between inconsistent ontologies and between incoherent terminologies, there has been some work on measuring inconsistency of an ontology and on measuring incoherency of a terminology. However, most of them merely focus either on measuring inconsistency or on measuring incoherency and no clear ideas of how to extend them to allow for the other. In this paper, we propose a novel approach to measure DL ontologies, named distance-based measures. It has the merits that both inconsistency and incoherency can be measured in a unified framework. Moreover, only classical DL interpretations are used such that there is no restriction on the DL languages used.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frederick Maier</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Distributed Reasoning with EL++ Using MapReduce</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Wright State University</style></publisher><pub-location><style face="normal" font="default" size="100%">Dayton, OH, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;It has recently been shown that the MapReduce framework for distributed computation can be used effectively for large-scale RDF Schema reasoning, computing the deductive closure of over a billion RDF triples within a reasonable time [23]. Later work has carried this approach over to OWL Horst [22]. In this paper, we provide a MapReduce algorithm for classifying knowledge bases in the description logic EL++, which is essentially the OWL 2 profile OWL 2 EL. The traditional EL++ classification algorithm is recast into a form compatible with MapReduce, and it is shown how the revised algorithm can be realized within the MapReduce framework. An analysis of the circumstances under which the algorithm can be effectively used is also provided.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jens Lehmann</style></author><author><style face="normal" font="default" size="100%">Sebastian Bader</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extracting Reduced Logic Programs from Artificial Neural Networks</style></title><secondary-title><style face="normal" font="default" size="100%">Applied Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s10489-008-0142-y</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">249–266</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Artificial neural networks can be trained to perform excellently in many application areas. Whilst they can learn from raw data to solve sophisticated recognition and analysis problems, the acquired knowledge remains hidden within the network architecture and is not readily accessible for analysis or further use: Trained networks are black boxes. Recent research efforts therefore investigate the possibility to extract symbolic knowledge from trained networks, in order to analyze, validate, and reuse the structural insights gained implicitly during the training process. In this paper, we will study how knowledge in form of propositional logic programs can be obtained in such a way that the programs are as simple as possible — where simple is being understood in some clearly defined and meaningful way.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prateek Jain</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Amit P. Sheth</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pavel Shvaiko</style></author><author><style face="normal" font="default" size="100%">Jérôme Euzenat</style></author><author><style face="normal" font="default" size="100%">Fausto Giunchiglia</style></author><author><style face="normal" font="default" size="100%">Heiner Stuckenschmidt</style></author><author><style face="normal" font="default" size="100%">Ming Mao</style></author><author><style face="normal" font="default" size="100%">Isabel F. Cruz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Flexible Bootstrapping-Based Ontology Alignment</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 5th International Workshop on Ontology Matching (OM-2010), Shanghai, China, November 7, 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-689/om2010_poster9.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Shanghai, China</style></pub-location><volume><style face="normal" font="default" size="100%">689</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;BLOOMS (Jain et al, ISWC2010, to appear) is an ontology alignment system which, in its core, utilizes the Wikipedia category hierarchy for establishing alignments. In this paper, we present a Plug-and-Play extension to BLOOMS, which allows to flexibly replace or complement the use of Wikipedia by other online or offline resources, including domain-specific ontologies or taxonomies. By making use of automated translation services and of Wikipedia in languages other than English, it makes it possible to apply BLOOMS to alignment tasks where the input ontologies are written in different languages.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anthony K. Seda</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Generalized Distance Functions in the Theory of Computation</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">denotational semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">fixed-point theorems</style></keyword><keyword><style  face="normal" font="default" size="100%">generalized distance functions</style></keyword><keyword><style  face="normal" font="default" size="100%">Logic Programming</style></keyword><keyword><style  face="normal" font="default" size="100%">stable model</style></keyword><keyword><style  face="normal" font="default" size="100%">supported model</style></keyword><keyword><style  face="normal" font="default" size="100%">topology</style></keyword><keyword><style  face="normal" font="default" size="100%">ultra-metrics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1093/comjnl/bxm108</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">53</style></volume><pages><style face="normal" font="default" size="100%">443–464</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We discuss a number of distance functions encountered in the theory of computation, including metrics, ultra-metrics, quasi-metrics, generalized ultra-metrics, partial metrics, d-ultra-metrics and generalized metrics. We consider their properties, associated fixed-point theorems and some general applications they have within the theory of computation. We consider in detail the applications of generalized distance functions in giving a uniform treatment of several important semantics for logic programs, including acceptable programs and natural generalizations of them, and also the supported model and the stable model in the context of locally stratified extended disjunctive logic programs and databases.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prateek Jain</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Peter Z. Yeh</style></author><author><style face="normal" font="default" size="100%">Kunal Verma</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Dan Brickley</style></author><author><style face="normal" font="default" size="100%">Vinay K. Chaudhri</style></author><author><style face="normal" font="default" size="100%">Harry Halpin</style></author><author><style face="normal" font="default" size="100%">Deborah McGuinness</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Linked Data Is Merely More Data</style></title><secondary-title><style face="normal" font="default" size="100%">Linked Data Meets Artificial Intelligence, Papers from the 2010 AAAI Spring Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/1130</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">AAAI</style></publisher><pub-location><style face="normal" font="default" size="100%">Stanford, California, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;In this position paper, we argue that the Linked Open Data (LoD) Cloud, in its current form, is only of limited value for furthering the Semantic Web vision. Being merely a weakly linked “triple collection,” it will only be of very limited bene- fit for the AI or Semantic Web communities. We describe the corresponding problems with the LoD Cloud and give directions for research to remedy the situation.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Frederick Maier</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Volker Haarslev</style></author><author><style face="normal" font="default" size="100%">David Toman</style></author><author><style face="normal" font="default" size="100%">Grant E. Weddell</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A MapReduce Algorithm for EL+</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 23rd International Workshop on Description Logics (DL 2010)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-573/paper_35.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Waterloo, Ontario, Canada</style></pub-location><volume><style face="normal" font="default" size="100%">573</style></volume><pages><style face="normal" font="default" size="100%">464-474</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recently, the use of the MapReduce framework for distributed RDF Schema reasoning has shown that it is possible to compute the deductive closure of sets of over a billion RDF triples within a reasonable time span [22], and that it is also possible to carry the approach over to OWL Horst [21]. Following this lead, in this paper we provide a MapReduce algorithm for the description logic EL+, more precisely for the classification of EL+ ontologies. To do this, we first modify the algorithm usually used for EL+ classification. The modified algorithm can then be converted into a MapReduce algorithm along the same key ideas as used for RDF schema.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Anthony K. Seda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mathematical Aspects of Logic Programming Semantics</style></title><secondary-title><style face="normal" font="default" size="100%">Studies in Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Chapman and Hall/CRC Press</style></publisher><pages><style face="normal" font="default" size="100%">304</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yan Wang</style></author><author><style face="normal" font="default" size="100%">Cong Wang</style></author><author><style face="normal" font="default" size="100%">Yi Zeng</style></author><author><style face="normal" font="default" size="100%">Zhisheng Huang</style></author><author><style face="normal" font="default" size="100%">Vassil Momtchev</style></author><author><style face="normal" font="default" size="100%">Bo Andersson</style></author><author><style face="normal" font="default" size="100%">Xu Ren</style></author><author><style face="normal" font="default" size="100%">Ning Zhong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Normalized MEDLINE Distance in Context-Aware Life Science Literature Searches</style></title><secondary-title><style face="normal" font="default" size="100%">The 4th Chinese Semantic Web Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Tsinghua Science &amp; Technology</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prateek Jain</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Kunal Verma</style></author><author><style face="normal" font="default" size="100%">Peter Z. Yeh</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Peter F. Patel-Schneider</style></author><author><style face="normal" font="default" size="100%">Yue Pan</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Peter Mika</style></author><author><style face="normal" font="default" size="100%">Lei Zhang</style></author><author><style face="normal" font="default" size="100%">Jeff Z. Pan</style></author><author><style face="normal" font="default" size="100%">Ian Horrocks</style></author><author><style face="normal" font="default" size="100%">Birte Glimm</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Alignment for Linked Open Data</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web - ISWC 2010 - 9th International Semantic Web Conference, ISWC 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-17746-0_26</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Shanghai, China</style></pub-location><volume><style face="normal" font="default" size="100%">6496</style></volume><pages><style face="normal" font="default" size="100%">402–417</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;The Web of Data currently coming into existence through the Linked Open Data (LOD) effort is a major milestone in realizing the Semantic Web vision. However, the development of applications based on LOD faces difficulties due to the fact that the different LOD datasets are rather loosely connected pieces of information. In particular, links between LOD datasets are almost exclusively on the level of instances, and schema-level information is being ignored. In this paper, we therefore present a system for finding schema-level links between LOD datasets in the sense of ontology alignment. Our system, called BLOOMS, is based on the idea of bootstrapping information already present on the LOD cloud. We also present a comprehensive evaluation which shows that BLOOMS outperforms state-of-the-art ontology alignment systems on LOD datasets. At the same time, BLOOMS is also competitive compared with these other systems on the Ontology Evaluation Alignment Initiative Benchmark datasets.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marco Gori</style></author><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Guenther Palm</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Perspectives and challenges for recurrent neural network training</style></title><secondary-title><style face="normal" font="default" size="100%">Logic Journal of the IGPL</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1093/jigpal/jzp042</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">617–619</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author><author><style face="normal" font="default" size="100%">Andreas Herzig</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Preface - Special issue on commonsense reasoning for the semantic web</style></title><secondary-title><style face="normal" font="default" size="100%">Annals of Mathematics and Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s10472-010-9209-7</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">58</style></volume><pages><style face="normal" font="default" size="100%">1–2</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Satya S. Sahoo</style></author><author><style face="normal" font="default" size="100%">Olivier Bodenreider</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Krishnaprasad Thirunarayan</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Michael Gertz</style></author><author><style face="normal" font="default" size="100%">Bertram Ludäscher</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biomedical knowledge repository</style></keyword><keyword><style  face="normal" font="default" size="100%">Context theory</style></keyword><keyword><style  face="normal" font="default" size="100%">Provenance context entity</style></keyword><keyword><style  face="normal" font="default" size="100%">Provenance Management Framework.</style></keyword><keyword><style  face="normal" font="default" size="100%">Provenir ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">RDF reification</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-13818-8_32</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg, Germany</style></pub-location><volume><style face="normal" font="default" size="100%">6187</style></volume><pages><style face="normal" font="default" size="100%">461–470</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;The Semantic Web Resource Description Framework (RDF) format is being used by a large number of scientific applications to store and disseminate their datasets. The provenance information, describing the source or lineage of the datasets, is playing an increasingly significant role in ensuring data quality, computing trust value of the datasets, and ranking query results. Current Semantic Web provenance tracking approaches using the RDF reification vocabulary suffer from a number of known issues, including lack of formal semantics, use of blank nodes, and application-dependent interpretation of reified RDF triples that hinders data sharing. In this paper, we introduce a new approach called Provenance Context Entity (PaCE) that uses the notion of provenance context to create provenance-aware RDF triples without the use of RDF reification or blank nodes. We also define the formal semantics of PaCE through a simple extension of the existing RDF(S) semantics that ensures compatibility of PaCE with existing Semantic Web tools and implementations. We have implemented the PaCE approach in the Biomedical Knowledge Repository (BKR) project at the US National Library of Medicine to support provenance tracking on RDF data extracted from multiple sources, including biomedical literature and the UMLS Metathesaurus. The evaluations demonstrate a minimum of 49% reduction in total number of provenancespecific RDF triples generated using the PaCE approach as compared to RDF reification. In addition, using the PACE approach improves the performance of complex provenance queries by three orders of magnitude and remains comparable to the RDF reification approach for simpler provenance queries.&amp;nbsp;&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Reasonable Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automated Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Formal Semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Linked Open Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2010-0010</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">39–44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as &quot;shared inference,&quot; which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on entirely different methods like machine learning or natureinspired computing need to be investigated, while quality assurance needs to be done in terms of precision and recall values (as in information retrieval) and not necessarily in terms of soundness and completeness of the underlying algorithms.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David W. Aha</style></author><author><style face="normal" font="default" size="100%">Mark S. Boddy</style></author><author><style face="normal" font="default" size="100%">Vadim Bulitko</style></author><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Prashant Doshi</style></author><author><style face="normal" font="default" size="100%">Stefan Edelkamp</style></author><author><style face="normal" font="default" size="100%">Christopher W. Geib</style></author><author><style face="normal" font="default" size="100%">Piotr J. Gmytrasiewicz</style></author><author><style face="normal" font="default" size="100%">Robert P. Goldman</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Charles L. Isbell</style></author><author><style face="normal" font="default" size="100%">Darsana P. Josyula</style></author><author><style face="normal" font="default" size="100%">Leslie Pack Kaelbling</style></author><author><style face="normal" font="default" size="100%">Kristian Kersting</style></author><author><style face="normal" font="default" size="100%">Maithilee Kunda</style></author><author><style face="normal" font="default" size="100%">Luís C. Lamb</style></author><author><style face="normal" font="default" size="100%">Bhaskara Marthi</style></author><author><style face="normal" font="default" size="100%">Keith McGreggor</style></author><author><style face="normal" font="default" size="100%">Vivi Nastase</style></author><author><style face="normal" font="default" size="100%">Gregory Provan</style></author><author><style face="normal" font="default" size="100%">Anita Raja</style></author><author><style face="normal" font="default" size="100%">Ashwin Ram</style></author><author><style face="normal" font="default" size="100%">Mark O. Riedl</style></author><author><style face="normal" font="default" size="100%">Stuart J. Russell</style></author><author><style face="normal" font="default" size="100%">Ashish Sabharwal</style></author><author><style face="normal" font="default" size="100%">Jan-Georg Smaus</style></author><author><style face="normal" font="default" size="100%">Gita Sukthankar</style></author><author><style face="normal" font="default" size="100%">Karl Tuyls</style></author><author><style face="normal" font="default" size="100%">Ron van der Meyden</style></author><author><style face="normal" font="default" size="100%">Alon Y. Halevy</style></author><author><style face="normal" font="default" size="100%">Lilyana Mihalkova</style></author><author><style face="normal" font="default" size="100%">Sriraam Natarajan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reports of the AAAI 2010 Conference Workshops</style></title><secondary-title><style face="normal" font="default" size="100%">AI Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.aaai.org/ojs/index.php/aimagazine/article/view/2318</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">95–108</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Web - Interoperability, Usability, Applicability</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2010-0017</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">1–2</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;The Semantic Web journal is set up to be a forum for highest-quality research contributions on all aspects of the Semantic Web. Its scope encompasses work in neighboring disciplines which is motivated by the Semantic Web vision. Besides the publishing of research contributions, it is also an outlet for reports on tools, systems, applications, and ontologies which enable research, rather than being direct research contributions. The journal also publishes top-quality surveys which serve as introductions to core topics of Semantic Web research.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Peter F. Patel-Schneider</style></author><author><style face="normal" font="default" size="100%">Yue Pan</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Peter Mika</style></author><author><style face="normal" font="default" size="100%">Lei Zhang</style></author><author><style face="normal" font="default" size="100%">Jeff Z. Pan</style></author><author><style face="normal" font="default" size="100%">Ian Horrocks</style></author><author><style face="normal" font="default" size="100%">Birte Glimm</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I</style></title><secondary-title><style face="normal" font="default" size="100%">9th International Semantic Web Conference, ISWC 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Shanghai, China</style></pub-location><volume><style face="normal" font="default" size="100%">6496</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Peter F. Patel-Schneider</style></author><author><style face="normal" font="default" size="100%">Yue Pan</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Peter Mika</style></author><author><style face="normal" font="default" size="100%">Lei Zhang</style></author><author><style face="normal" font="default" size="100%">Jeff Z. Pan</style></author><author><style face="normal" font="default" size="100%">Ian Horrocks</style></author><author><style face="normal" font="default" size="100%">Birte Glimm</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part II</style></title><secondary-title><style face="normal" font="default" size="100%">9th International Semantic Web Conference, ISWC 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Shanghai, China</style></pub-location><volume><style face="normal" font="default" size="100%">6497</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xu Ren</style></author><author><style face="normal" font="default" size="100%">Yi Zeng</style></author><author><style face="normal" font="default" size="100%">Yulin Qin</style></author><author><style face="normal" font="default" size="100%">Ning Zhong</style></author><author><style face="normal" font="default" size="100%">Zhisheng Huang</style></author><author><style face="normal" font="default" size="100%">Yan Wang</style></author><author><style face="normal" font="default" size="100%">Cong Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Social Relation Based Search Refinement: Let Your Friends Help You!</style></title><secondary-title><style face="normal" font="default" size="100%">Active Media Technology, 6th International Conference, AMT 2010, Toronto, Canada, August 28-30, 2010. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-15470-6_48</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">475–485</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Targeted Ontology Matching</style></title><secondary-title><style face="normal" font="default" size="100%">International Symposium on Collaborative Technologies and Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pub-location><style face="normal" font="default" size="100%">Chicago, IL</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yi Zeng</style></author><author><style face="normal" font="default" size="100%">Yan Wang</style></author><author><style face="normal" font="default" size="100%">Zhisheng Huang</style></author><author><style face="normal" font="default" size="100%">Danica Damljanovic</style></author><author><style face="normal" font="default" size="100%">Ning Zhong</style></author><author><style face="normal" font="default" size="100%">Cong Wang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">User Interests: Definition, Vocabulary, and Utilization in Unifying Search and Reasoning</style></title><secondary-title><style face="normal" font="default" size="100%">Active Media Technology, 6th International Conference, AMT 2010, Toronto, Canada, August 28-30, 2010. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-15470-6_11</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">98–107</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Thomas Lukasiewicz</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems. Fourth International Conference, RR 2010, Bressanone, Italy, September 22-24, 2010, Proceedings</style></title><secondary-title><style face="normal" font="default" size="100%">4th International Conference on Web Reasoning and Rule Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Bressanone, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">6333</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yue Ma</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author><author><style face="normal" font="default" size="100%">Guohui Xiao</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Zuoquan Lin</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Dimitris Karagiannis</style></author><author><style face="normal" font="default" size="100%">Zhi Jin</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Anytime Algorithm for Computing Inconsistency Measurement</style></title><secondary-title><style face="normal" font="default" size="100%">Knowledge Science, Engineering and Management, Third International Conference, KSEM 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-10488-6_7</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Vienna, Austria</style></pub-location><volume><style face="normal" font="default" size="100%">5914</style></volume><pages><style face="normal" font="default" size="100%">29–40</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Measuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first analyze its computational complexity. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximation of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Ben Goertzel</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Marcus Hutter</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Artificial General Intelligence. Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009. Proceedings</style></title><secondary-title><style face="normal" font="default" size="100%">Second Conference on Artificial General Intelligence, AGI 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Atlantis Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Arlington, Virginia, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Henrik Schärfe</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Conceptual Structures in Practice</style></title><secondary-title><style face="normal" font="default" size="100%">Studies in Informatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Chapman and Hall/CRC</style></publisher><pages><style face="normal" font="default" size="100%">425</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gaston Tagni</style></author><author><style face="normal" font="default" size="100%">Christophe Gueret</style></author><author><style face="normal" font="default" size="100%">Stefan Schlobach</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Evolutionary Computing Approach for Reasoning in the Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Poster at DECOI2009, the International Workshop on Collective Intelligence andEvolution</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pub-location><style face="normal" font="default" size="100%">Leiden, The Netherlands</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Kai-Uwe Kühnberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Facets of Artificial General Intelligence</style></title><secondary-title><style face="normal" font="default" size="100%">Künstliche Intelligenz</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.kuenstliche-intelligenz.de/fileadmin/template/main/archiv/pdf/ki2009-02_page58-59_web_full.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">58–59</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We argue that time has come for a serious endeavor to work towards artificial general intelligence (AGI). This positive assessment of the very possibility of AGI has partially its roots in the development of new methodological achievements in the AI area, like new learning paradigms and new integration techniques for different methodologies. The article sketches some of these methods as prototypical examples for approaches towards AGI.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Foundations of Semantic Web Technologies</style></title><secondary-title><style face="normal" font="default" size="100%">Textbooks in Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Chapman and Hall/CRC Press</style></publisher><pages><style face="normal" font="default" size="100%">455</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Kai-Uwe Kühnberger</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ben Goertzel</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Marcus Hutter</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The Importance of Being Neural-Symbolic – A Wilde Position</style></title><secondary-title><style face="normal" font="default" size="100%">Second Conference on Artificial General Intelligence, AGI 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pub-location><style face="normal" font="default" size="100%">Arlington, Virginia, USA</style></pub-location><pages><style face="normal" font="default" size="100%">208-209</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We argue that Neural-Symbolic Integration is a topic of central importance for the advancement of Artificial General Intelligence.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brigitte Endres-Niggemeyer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Valentin Zacharias</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">KI 2009 - AI Mashup Challenge 2009</style></title><secondary-title><style face="normal" font="default" size="100%">KI - Künstliche Intelligenz</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">52</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Bijan Parsia</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Steffen Staab</style></author><author><style face="normal" font="default" size="100%">Rudi Studer</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontologies and Rules</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook on Ontologies</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><edition><style face="normal" font="default" size="100%">2</style></edition><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">111-132</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Satya S. Sahoo</style></author><author><style face="normal" font="default" size="100%">D. Brent Weatherly</style></author><author><style face="normal" font="default" size="100%">Pramod Anantharam</style></author><author><style face="normal" font="default" size="100%">Flora Logan</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Rick Tarleton</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Driven Integration of Biology Experiment Data</style></title><secondary-title><style face="normal" font="default" size="100%">Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters &amp; Demos</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pub-location><style face="normal" font="default" size="100%">Cleveland, OH, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Satya S. Sahoo</style></author><author><style face="normal" font="default" size="100%">D. Brent Weatherly</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pramod Anantharam</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Rick Tarleton</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Robert Meersman</style></author><author><style face="normal" font="default" size="100%">Tharam S. Dillon</style></author><author><style face="normal" font="default" size="100%">Pilar Herrero</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology-Driven Provenance Management in eScience: An Application in Parasite Research</style></title><secondary-title><style face="normal" font="default" size="100%">On the Move to Meaningful Internet Systems: OTM 2009, Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009, Proceedings, Part II</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-05151-7_18</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Vilamoura, Portugal</style></pub-location><volume><style face="normal" font="default" size="100%">5871</style></volume><pages><style face="normal" font="default" size="100%">992–1009</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Provenance, from the French word “provenir”, describes the lineage or history of a data entity. Provenance is critical information in scientific applications to verify experiment process, validate data quality and associate trust values with scientific results. Current industrial scale eScience projects require an end-to-end provenance management infrastructure. This infrastructure needs to be underpinned by formal semantics to enable analysis of large scale provenance information by software applications. Further, effective analysis of provenance information requires well-defined query mechanisms to support complex queries over large datasets. This paper introduces an ontology-driven provenance management infrastructure for biology experiment data, as part of the Semantic Problem Solving Environment (SPSE) for Trypanosoma cruzi (T.cruzi). This provenance infrastructure, called T.cruzi Provenance Management System (PMS), is underpinned by (a) a domain-specific provenance ontology called Parasite Experiment ontology, (b) specialized query operators for provenance analysis, and (c) a provenance query engine. The query engine uses a novel optimization technique based on materialized views called materialized provenance views (MPV) to scale with increasing data size and query complexity. This comprehensive ontology-driven provenance infrastructure not only allows effective tracking and management of ongoing experiments in the Tarleton Research Group at the Center for Tropical and Emerging Global Diseases (CTEGD), but also enables researchers to retrieve the complete provenance information of scientific results for publication in literature.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>12</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Bijan Parsia</style></author><author><style face="normal" font="default" size="100%">Peter F. Patel-Schneider</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">OWL 2 Web Ontology Language: Primer</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/27/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.w3.org/TR/2009/REC-owl2-primer-20091027</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">W3C Recommendation</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yue Ma</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Axel Polleres</style></author><author><style face="normal" font="default" size="100%">Terrance Swift</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Paraconsistent Reasoning for OWL 2</style></title><secondary-title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems, Third International Conference, RR 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-05082-4_14</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Chantilly, VA, USA</style></pub-location><volume><style face="normal" font="default" size="100%">5837</style></volume><pages><style face="normal" font="default" size="100%">197–211</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;A four-valued description logic has been proposed to reason with description logic based inconsistent knowledge bases. This approach has a distinct advantage that it can be implemented by invoking classical reasoners to keep the same complexity as under the classical semantics. However, this approach has so far only been studied for the basid description logic ALC. In this paper, we further study how to extend the four-valued semantics to the more expressive description logic SROIQ which underlies the forthcoming revision of the Web Ontology Language, OWL 2, and also investigate how it fares when adapated to tractable description logics including EL++, DL-Lite, and Horn-DLs. We define the four-valued semantics along the same lines as for ALC and show that we can retain most of the desired properties.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephan Grimm</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Axel Polleres</style></author><author><style face="normal" font="default" size="100%">Terrance Swift</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Preferential Tableaux Calculus for Circumscriptive ALCO</style></title><secondary-title><style face="normal" font="default" size="100%">Web Reasoning and Rule Systems, Third International Conference, RR 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-05082-4_4</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Chantilly, VA, USA</style></pub-location><volume><style face="normal" font="default" size="100%">5837</style></volume><pages><style face="normal" font="default" size="100%">40–54</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;Nonmonotonic extensions of description logics (DLs) allow for default and local closed-world reasoning and are an acknowledged desired feature for applications, e.g. in the Semantic Web. A recent approach to such an extension is based on McCarthy’s circumscription, which rests on the principle of minimising the extension of selected predicates to close off dedicated parts of a domain model. While decidability and complexity results have been established in the literature, no practical algorithmisation for circumscriptive DLs has been proposed so far. In this paper, we present a tableaux calculus that can be used as a decision procedure for concept satisfiability with respect to conceptcircumscribed ALCO knowledge bases. The calculus builds on existing tableaux for classical DLs, extended by the notion of a preference clash to detect the non-minimality of constructed models.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Artur S. d'Avila Garcez</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings of the Fifth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'09, at the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, July 2009</style></title><secondary-title><style face="normal" font="default" size="100%">Fifth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy'09, at the 21st International Joint Conference on Artificial Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-481</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Pasadena, California</style></pub-location><volume><style face="normal" font="default" size="100%">481</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Stephan Grimm</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings of the Fourth International Workshop on Applications of Semantic Technologies, AST2009, at Informatik2009, Lübeck, Germany, October 2009</style></title><secondary-title><style face="normal" font="default" size="100%">INFORMATIK 2009 - Im Fokus das Leben</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Bonner Köllen Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Lübeck, Germany</style></pub-location><pages><style face="normal" font="default" size="100%">381-400</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Qiu Ji</style></author><author><style face="normal" font="default" size="100%">Peter Haase</style></author><author><style face="normal" font="default" size="100%">Guilin Qi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Steffen Stadtmüller</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lora Aroyo</style></author><author><style face="normal" font="default" size="100%">Paolo Traverso</style></author><author><style face="normal" font="default" size="100%">Fabio Ciravegna</style></author><author><style face="normal" font="default" size="100%">Philipp Cimiano</style></author><author><style face="normal" font="default" size="100%">Tom Heath</style></author><author><style face="normal" font="default" size="100%">Eero Hyvönen</style></author><author><style face="normal" font="default" size="100%">Riichiro Mizoguchi</style></author><author><style face="normal" font="default" size="100%">Eyal Oren</style></author><author><style face="normal" font="default" size="100%">Marta Sabou</style></author><author><style face="normal" font="default" size="100%">Elena Paslaru Bontas Simperl</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">RaDON - Repair and Diagnosis in Ontology Networks</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-02121-3_71</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heraklion, Crete, Greece</style></pub-location><volume><style face="normal" font="default" size="100%">5554</style></volume><pages><style face="normal" font="default" size="100%">863–867</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;One of the major challenges in managing networked and dynamic ontologies is to handle inconsistencies in single ontologies, and inconsistencies introduced by integrating multiple distributed ontologies. Our RaDON system provides functionalities to repair and diagnose ontology networks by extending the capabilities of existing reasoners. The system integrates several new debugging and repairing algorithms, such as a relevance-directed algorithm to meet the various needs of the users.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meenakshi Nagarajan</style></author><author><style face="normal" font="default" size="100%">Karthik Gomadam</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Ajith Ranabahu</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Ashutosh Jadhav</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gottfried Vossen</style></author><author><style face="normal" font="default" size="100%">Darrell D. E. Long</style></author><author><style face="normal" font="default" size="100%">Jeffrey Xu Yu</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences</style></title><secondary-title><style face="normal" font="default" size="100%">Web Information Systems Engineering - WISE 2009, 10th International Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-04409-0_52</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Poznan, Poland</style></pub-location><volume><style face="normal" font="default" size="100%">5802</style></volume><pages><style face="normal" font="default" size="100%">539–553</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We present work in the spatio-temporal-thematic analysis of citizen-sensor observations pertaining to real-world events. Using Twitter as a platform for obtaining crowd-sourced observations, we explore the interplay between these 3 dimensions in extracting insightful summaries of social perceptions behind events. We present our experiences in building a web mashup application, Twitris [1] that extracts and facilitates the spatio-temporal-thematic exploration of event descriptor summaries.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Rinke Hoekstra</style></author><author><style face="normal" font="default" size="100%">Peter F. Patel-Schneider</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Suggestions for OWL 3</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 5th International Workshop on {OWL:} Experiences and Directions {(OWLED} 2009), Chantilly, VA, United States, October 23-24, 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-529/owled2009_submission_6.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Chantilly, VA, United States</style></pub-location><volume><style face="normal" font="default" size="100%">529</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;With OWL 2 about to be completed, it is the right time to start discussions on possible future modifications of OWL. We present here a number of suggestions in order to discuss them with the OWL user community. They encompass expressive extensions on polynomial OWL 2 profiles, a suggestion for an OWL Rules language, and expressive extensions for OWL DL.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Krzysztof Janowicz</style></author><author><style face="normal" font="default" size="100%">Martin Raubal</style></author><author><style face="normal" font="default" size="100%">Sergei Levashkin</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Reasoning Pragmatics</style></title><secondary-title><style face="normal" font="default" size="100%">GeoSpatial Semantics, Third International Conference, GeoS 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-10436-7_2</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Mexico City, Mexico</style></pub-location><volume><style face="normal" font="default" size="100%">5892</style></volume><pages><style face="normal" font="default" size="100%">9–25</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which force us to question established lines of research and to rethink the underlying approaches.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pramod Anantharam</style></author><author><style face="normal" font="default" size="100%">Satya S. Sahoo</style></author><author><style face="normal" font="default" size="100%">D. Brent Weatherly</style></author><author><style face="normal" font="default" size="100%">Flora Logan</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Rick Tarleton</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Trykipedia: Collaborative Bio-Ontology Development using Wiki Environment</style></title><secondary-title><style face="normal" font="default" size="100%">Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters &amp; Demos</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pub-location><style face="normal" font="default" size="100%">Cleveland, OH, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ashutosh Jadhav</style></author><author><style face="normal" font="default" size="100%">Wenbo Wang</style></author><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pramod Anantharam</style></author><author><style face="normal" font="default" size="100%">Vinh Nguyen</style></author><author><style face="normal" font="default" size="100%">Amit Sheth</style></author><author><style face="normal" font="default" size="100%">Karthik Gomadam</style></author><author><style face="normal" font="default" size="100%">Meenakshi Nagarajan</style></author><author><style face="normal" font="default" size="100%">Ajith Ranabahu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Twitris: Socially Influenced Browsing</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web Challenge at the 8th International Semantic Web Conference (ISWC 2009)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pub-location><style face="normal" font="default" size="100%">Washington DC, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;In this paper, we present Twitris, a semantic Web application that facilitates browsing for news and information, using social perceptions as the fulcrum. In doing so we address challenges in large scale crawling, processing of real time information, and preserving spatiotemporal-thematic properties central to observations pertaining to realtime events. We extract metadata about events from Twitter and bring related news and Wikipedia articles to the user. In developing Twitris, we have used the DBPedia ontology.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Sebastian Rudolph</style></author><author><style face="normal" font="default" size="100%">York Sure</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Web Grundlagen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer textbook</style></publisher><pages><style face="normal" font="default" size="100%">277</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Carsten Lutz</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Nachum Dershowitz</style></author><author><style face="normal" font="default" size="100%">Andrei Voronkov</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Complexity in the EL Family of Description Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Logic for Programming, Artificial Intelligence, and Reasoning, 14th International Conference, LPAR 2007, Yerevan, Armenia, October 15-19, 2007, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-540-75560-9_25</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4790</style></volume><pages><style face="normal" font="default" size="100%">333-347</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We study the data complexity of instance checking and conjunctive query answering in the EL family of description logics, with a particular emphasis on the boundary of tractability. We identify a large number of intractable extensions of EL, but also show that in ELIf , the extension of EL with inverse roles and global functionality, conjunctive query answering is tractable regarding data complexity. In contrast, already instance checking in EL extended with only inverse roles or global functionality is EXPTIME-complete regarding combined complexity</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Carsten Lutz</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Diego Calvanese</style></author><author><style face="normal" font="default" size="100%">Enrico Franconi</style></author><author><style face="normal" font="default" size="100%">Volker Haarslev</style></author><author><style face="normal" font="default" size="100%">Domenico Lembo</style></author><author><style face="normal" font="default" size="100%">Boris Motik</style></author><author><style face="normal" font="default" size="100%">Anni-Yasmin Turhan</style></author><author><style face="normal" font="default" size="100%">Sergio Tessaris</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Complexity in the EL family of DLs</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2007 International Workshop on Description Logics (DL2007), Brixen-Bressanone, near Bozen-Bolzano, Italy, 8-10 June, 2007</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-250/paper_15.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><volume><style face="normal" font="default" size="100%">250</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Complexity of Instance Checking in the EL Family of Description Logics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://lat.inf.tu-dresden.de/research/mas/#Kri-Mas-07</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Technische Universität Dresden</style></publisher><pub-location><style face="normal" font="default" size="100%">Dresden</style></pub-location><volume><style face="normal" font="default" size="100%">Master of Science</style></volume><pages><style face="normal" font="default" size="100%">v+68</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Subsumption in the description logic (DL) EL is known to be tractable even when it is done with respect to the most general form of terminology, namely a set of general inclusion axioms (GCIs). Recently, this tractability boundary has been clarified by identifying DL constructors that causes intractability of subsumption when added to EL and that do not. These results provide us with a characterization of the complexity of subsumption for the EL family of DLs (i.e., EL and its extensions).

Besides subsumption, there are other standard reasoning problems studied in DL. Among them, the instance checking problem is the most basic reasoning problem that is concerned with deriving implicit knowledge about individuals in a DL knowledge base. Such a knowledge base consists of an intensional part in the form of a terminology (TBox) and an extensional or data part in the form of assertions about particular individuals in the domain of the knowledge base (ABox). Like other reasoning problems, complexity of instance checking is usually measured in the size of the whole input - thus called combined complexity - which, in this case, consists of a TBox, an ABox, a query concept and an individual name. On the other hand, it is common to assume that the data (ABox) is very large compared to the TBox and the query. Therefore, it is often more realistic to use a complexity measure based only on the size of the ABox, i.e., data complexity.

For the EL family, results for the combined complexity of instance checking can be derived from the complexity results for subsumption. But results which are concerned with data complexity are still lacking. This motivates us to investigate the data complexity of instance checking in the EL family. In particular, we are interested in whether there are extensions of EL which are intractable regarding combined complexity, but tractable regarding data complexity.

The first part of this thesis establishes coNP-hardness (and even coNP-completeness) results regarding data complexity of instance checking w.r.t. sets of GCIs for extensions of EL with negation, disjunction, value restriction, number restriction and role constructors such as role negation, role union and transitive closures. The lower bounds of data complexity for these DLs are proved by polynomial reductions from the complement of 2+2-SAT, a variant of propositional satisfiability problem which is NP-complete, whereas the upper bounds follow from known results of data complexity for ALC and SHIQ.

The second part identifies an extension of EL called ELIf, for which data complexity of instance checking w.r.t. sets of GCIs is tractable. The DL ELIf is obtained from EL by adding inverse roles and global functionality. This result is interesting since adding only one of those two constructors leads to intractability of reasoning w.r.t. combined complexity. The result is derived by giving an algorithm that decides instance checking in ELIf w.r.t. sets of GCIs and runs in time polynomial in the size of the input ABox.</style></abstract><work-type><style face="normal" font="default" size="100%">Master's</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Felicia Harlow</style></author><author><style face="normal" font="default" size="100%">Kevin Cleereman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Feature Selection for Collaborative Team Formation via Social Network Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Data Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pub-location><style face="normal" font="default" size="100%">Las Vegas, NV</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Kevin Cleereman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of Social Network Analysis to Collaborative Team Formation</style></title><secondary-title><style face="normal" font="default" size="100%">International Symposium on Collaborative Technologies and Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><pub-location><style face="normal" font="default" size="100%">Las Vegas, NV</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Mateen Rizki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Feature and Prototype Evolution for Nearest Neighbor Classification of Web Documents</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Information Technology - New Generations</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><pub-location><style face="normal" font="default" size="100%">Las Vegas, NV</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Banshi D. Chaudhary</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Traceability from Use Case to .NET Assembly via Design Patterns</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Motilal Nehru National Institute of Technology (MNNIT)</style></publisher><pub-location><style face="normal" font="default" size="100%">Allahabad, India</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Michael Cox</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AI 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