@inbook {800, title = {Ontology-based Data Organization for the Enslaved Project}, year = {In Press}, author = {Cogan Shimizu and Pascal Hitzler} } @inbook {994, title = {Neurosymbolic Hidden Neuron Analysis in Convolutional Neural Networks}, booktitle = {Neuro-Symbolic AI: Bridging the Gap Between Neural Networks and Symbolic Reasoning}, year = {2026}, publisher = {Elsevier}, organization = {Elsevier}, chapter = {8}, abstract = {

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.

}, author = {Abhilekha Dalal and Moumita Sen Sarma and Avishek Das and Samatha E. Akkamahadevi and Eugene Y. Vasserman and Pascal Hitzler} } @inbook {995, title = {Symbolic Reasoning Methods in Neural Architectures and Large Language Models}, booktitle = {Neuro-Symbolic AI: Bridging the Gap Between Neural Networks and Symbolic Reasoning}, year = {2026}, chapter = {5}, abstract = {

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.

}, author = {Adrita Barua and Avishek Das and Moumita Sen Sarma and Pascal Hitzler} } @conference {992, title = {Complex Ontology Alignment using LLMs: A Case Study}, booktitle = {The 20th International Workshop on Ontology Matching collocated with the 24th International Semantic Web Conference ISWC-2025}, year = {2025}, address = {Nara, Japan}, author = {Adrita Barua and Reza Amini and Sanaz Saki Norouzi and Reihaneh Amini and Pascal Hitzler} } @article {986, title = {Deep Deductive Reasoning is a Hard Deep Learning Problem}, journal = {Neurosymbolic Artificial Intelligence}, year = {2025}, author = {Pascal Hitzler and Rushrukh Rayan and Joseph Zalewski and Sanaz Saki Norouzi and Aaron Eberhart and Eugene Y. Vasserman} } @conference {991, title = {Description Logic Concept Learning using Large Language Models}, booktitle = {19th International Conference on Neurosymbolic Learning and Reasoning}, volume = {284:1{\textendash}19, }, year = {2025}, address = {Santa Cruz, California, USA}, url = {https://openreview.net/forum?id=ebVC7S5VMF}, author = {Adrita Barua and Pascal Hitzler} } @conference {996, title = {Hidden Neuron Activation Analysis on Labeled Text Data}, booktitle = {K-CAP {\textquoteright}25: Knowledge Capture Conference 2025}, year = {2025}, month = {12/2025}, pages = {206 - 210}, publisher = {ACM}, organization = {ACM}, address = {USA}, abstract = {

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{\textquoteright}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.

}, keywords = {Concept-based Explanation, Dense Layer Analysis, Explainable AI, Hidden Neuron Analysis}, author = {Avishek Das and Abhilekha Dalal and Pascal Hitzler} } @booklet {990, title = {An Ontology for Representing Curriculum and Learning Material}, year = {2025}, abstract = {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. }, keywords = {education, modular ontology design, ontology}, doi = {10.48550/arXiv.2506.05751}, url = {https://arxiv.org/abs/2506.05751}, author = {Antrea Christou and Chris Davis Jaldi and Joseph Zalewski and Hande Kucuk McGinty and Pascal Hitzler and Cogan Shimizu} } @conference {979, title = {Automating CNN Neuron Interpretation using Concept Induction}, booktitle = {THE 23RD INTERNATIONAL SEMANTIC WEB CONFERENCE, ISWC 2024}, year = {2024}, abstract = {

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.

}, keywords = {Automation in AI, Deep Learning, Explainable Artificial Intelligence, Knowledge Graph, Semantic Web}, author = {Samatha Ereshi Akkamahadevi and Abhilekha Dalal and Pascal Hitzler} } @proceedings {954, title = {A Case for Extensional Non-Wellfounded Metamodeling}, journal = {LPAR 25}, volume = {18}, year = {2024}, month = {05/2024}, pages = {163--178}, publisher = {EasyChair}, edition = {Kalpa Publications in Computing, LPAR 25 Complementary Volume}, address = {Balaclava, Mauritius}, abstract = {

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.

}, doi = {10.29007/bpwd}, url = {https://easychair.org/publications/paper/K8rh}, author = {Joseph Zalewski and Pascal Hitzler} } @conference {984, title = {Commonsense Ontology Micropatterns}, booktitle = {18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024}, year = {2024}, publisher = {Springer}, organization = {Springer}, address = {Barcelona, ES}, doi = {10.1007/978-3-031-71170-1_6}, author = {Andrew Eells and Dave, B and Pascal Hitzler and Cogan Shimizu} } @conference {976, title = {Concept Induction using LLMs}, booktitle = {The 23rd International Semantic Web Conference, ISWC 2024}, year = {2024}, publisher = { CEUR-WS.org}, organization = { CEUR-WS.org}, address = {Maryland, USA}, author = {Adrita Barua} } @conference {975, title = {Concept Induction using LLMs: A user experiment for assessment}, booktitle = {Proceedings of the 18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024}, year = {2024}, publisher = {Springer Lecture Notes in Computer Science, to appear.}, organization = {Springer Lecture Notes in Computer Science, to appear.}, address = {Barcelona, Spain, September }, author = {Adrita Barua and Cara Widmer and Pascal Hitzler} } @proceedings {985, title = {DaSeLab at LLMs4OL 2024 Task A: Towards Term Typing in Ontology Learning}, journal = {The 23rd Internatinal Semantic Web Conference, ISWC 2024}, volume = {1st LLMs4OL Challenge @ ISWC 2024}, year = {2024}, publisher = {TIB Open Access Publishing proceedings}, address = { Maryland, USA}, author = {Adrita Barua and Saki Norouzi, Sanaz and Hitzler, Pascal} } @proceedings {973, title = {Error-margin Analysis for Hidden Neuron Activation Labels}, journal = {18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024}, year = {2024}, publisher = {Springer }, abstract = {

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{\textquoteright}s error margin.

}, keywords = {CNN, Concept Induction, Explainable AI}, author = {Abhilekha Dalal and Rushrukh Rayan and Pascal Hitzler} } @conference {989, title = {The HIP Ontology: a formal framework to support disaster risk reduction and management}, booktitle = {JOWO 2024 at FOIS 2024}, year = {2024}, author = {Shirly Stephen and Mark Schildhauer and Krzysztof Janowicz and Kitty Currier and Pascal Hitzler and Cogan Shimizu and Colby K. Fisher and Dean Rehberger} } @article {987, title = {Neuro-Symbolic AI and the Semantic Web}, journal = {Semantic Web}, year = {2024}, author = {Pascal Hitzler and Monireh Ebrahimi and Md Kamruzzaman Sarker and Daria Stepanova} } @article {997, title = {Ontology Modules for Grain Supply Chain Tracing}, year = {2024}, author = {Cogan Shimizu and Abhilehka Dalal and Pascal Hitzler and Evan Wallace and Frank Riddick and Scott Niemann and Joe Tevis and Farhad Ameri} } @article {988, title = {Towards a Global Food Systems Datahub}, journal = {Semantic Web Journal}, year = {2024}, author = {Hande Kucuk McGinty and Cogan Shimizu and Pascal Hitzler and Ajay Sharda} } @conference {971, title = {Towards Complex Ontology Alignment using Large Language Models}, year = {2024}, author = {Reihaneh Amini and Saki Norouzi, Sanaz and Pascal Hitzler and Reza Amini} } @proceedings {974, title = {Understanding CNN Hidden Neuron Activations using Concept Induction over Background Knowledge}, journal = {THE 23RD INTERNATIONAL SEMANTIC WEB CONFERENCE, ISWC 2024}, year = {2024}, abstract = {

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{\textquoteright}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.

}, keywords = {Concept Induction, Convolutional Neural Network, Explainable AI, Knowledge Graph}, author = {Abhilehka Dalal} } @mastersthesis {993, title = {Understanding Hidden Neuron Activations Using Structured Background Knowledge and Deductive Reasoning}, volume = {PhD}, year = {2024}, abstract = {

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.

In addition, we argue that understanding neuron behavior requires not only identifying what activates a neuron (recall) but also examining its precision{\textemdash}how it responds to other stimuli, which we define as the neuron{\textquoteright}s error margin, enhancing the granularity of neuron interpretation.

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.

}, author = {Abhilekha Dalal} } @conference {972, title = {On the Value of Labeled Data and Symbolic Methods for Hidden Neuron Activation Analysis}, booktitle = {18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024}, year = {2024}, publisher = {Springer }, organization = {Springer }, abstract = {

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.

}, keywords = {CNN, Concept Induction, Explainable AI, LLM}, author = {Abhilekha Dalal and Rushrukh Rayan and Adrita Barua and Eugene Y. Vasserman and Kamruzzaman Sarker and Pascal Hitzler} } @article {913, title = {Bridging RDF and Property Graphs: Linking KnowWhereGraph and SPOKE}, year = {2023}, author = {Shirly Stephen and Cogan Shimizu and Pascal Hitzler and Karthik Soman and Peter W Rose and John H Morris and Sergio E Baranzini and Krzysztof Janowicz and Antrea Christou and Abhilekha Dalal and Kitty Currier and Mark Schildhauer} } @article {934, title = {Conversational Ontology Alignment with ChatGPT}, year = {2023}, author = {Sanaz Saki Norouzi and Mohammad Saeid Mahdavinejad and Pascal Hitzler} } @article {969, title = {Deep Deductive Reasoning is a Hard Deep Learning Problem}, year = {2023}, author = {Pascal Hitzler and Rushrukh Rayan and Joseph Zalewski and Sanaz Saki Norouzi and Aaron Eberhart and Eugene Y. Vasserman} } @proceedings {957, title = {Expertise Ontology: Modeling Expertise in the Context of Emergency Management}, journal = {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)}, year = {2023}, publisher = {CEUR Workshop Proceedings 3637, CEUR-WS.org 2023.}, address = {Sherbrooke, Quebec, Canada}, author = {Shirly Stephen and Kitty Currier and Mark Schildhauer and Ling Cai and Yuanyuan Tian and Cogan Shimizu and Kryzstof Janowicz and Pascal Hitzler and Anna Lopez-Carr and Andrew Schroeder and Rui Zhu and Dean Rehberger and Gengchen Mai and Zilong Liu and Colby Fisher} } @article {905, title = {Explaining Deep Learning Hidden Neuron Activations using Concept Induction}, year = {2023}, abstract = {

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.

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.\ 

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\ based on description logics that was originally developed for applications in the Semantic Web field.\ 

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.

}, author = {Abhilekha Dalal and Md Kamruzzaman Sarker and Adrita Barua and Pascal Hitzler} } @inbook {955, title = {Fast Forward from Data to Insight: (Geographic) Knowledge Graphs and Their Applications. }, booktitle = {Handbook of Geospatial Artificial Intelligence,CRC Press}, year = {2023}, pages = {411-426}, edition = {In: Song Gao, Yinjie Hu, Wenwen Li (eds.)}, author = {Krzysztof Janowicz and Kitty Currier and Cogan Shimizu and Rui Zhu and Meilin Shi and Colby K. Fisher and Dean Rehberger and Pascal Hitzler and Zilong Liu and Shirly Stephen} } @proceedings {956, title = {A Formal Framework for Disaster Risk Properties}, journal = {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)}, year = {2023}, publisher = {CEUR Workshop Proceedings 3637,CEUR-WS.org 2023.}, address = {Sherbrooke, Quebec, Canada}, author = {Shirly Stephen and Mark Schildhauer and Kitty Currier and Pascal Hitzler and Cogan Shimizu and Krzysztof Janowicz and Dean Rehberger} } @article {906, title = {The KnowWhereGraph Ontology}, year = {2023}, author = {Cogan Shimizu and Shirly Stephen and Kitty Currier and Pascal Hitzler and Rui Zhu and Krzysztof Janowicz and Mark Schildhauer and Mohammad Saeid Mahdavinejad and Abhilekha Dalal and Adrita Barua and Ling Cai and Gengchen Mai and Zhangyu Wang and Yuanyuan Tian and Sanaz Saki Norouzi and Zilong Liu and Meilin Shi and Colby K. Fisher} } @conference {916, title = {The KnowWhereGraph Ontology: A Showcase}, year = {2023}, author = {Cogan Shimizu and Shirly Stephen and Rui Zhu and Kitty Currier and Mark Schildhauer and Dean Rehberger and Pascal Hitzler and Krzysztof Janowicz and Colby K. Fisher and Mohammad Saeid Mahdavinejad and Antrea Christou and Adrita Barua and Abhilekha Dalal and Sanaz Saki Norouzi and Zilong Liu and Meilin Shi and Ling Cai and Gengchen Mai and Zhangyu Wang and Yuanyuan Tian} } @proceedings {958, title = {The KnowWhereGraph Ontology: A Showcase}, journal = {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),}, year = {2023}, month = {2023}, publisher = {CEUR Workshop Proceedings 3637, CEUR-WS.org 2023.}, address = {Sherbrooke, Quebec, Canada}, author = {Cogan Shimizu and Shirly Stephen and Rui Zhu and Kitty Currier and Mark Schildhauer and Dean Rehberger and Krzysztof Janowicz and Pascal Hitzler and Colby Fisher and Mohammad Saeid Mahdavinejad and Antrea Christou and Adrita Barua and Abhilehka Dalal and Sanaz Saki Norouzi and Yuanyuan Tian and Ling Cai and Gengchen Mai and Zhangyu Wang and Zilong Liu and Meilin Shi} } @conference {914, title = {KnowWhereGraph-Lite: A Perspective of the KnowWhereGraph}, booktitle = {KGSWC 2023}, year = {2023}, author = {Cogan Shimizu and Shirly Stephen and Antrea Christou and Kitty Currier and Mohammad Saeid Mahdavinejad and Sanaz Saki Norouzi and Abhilekha Dalal and Adrita Barua and Colby K. Fisher and Anthony D{\textquoteright}Onofrio and Thomas Thelen and Krzysztof Janowicz and Dean Rehberger and Mark Schildhauer and Pascal Hitzler} } @article {912, title = {MMODS-O: A Modular Ontology for the Metadata Object Description Schema (MODS) {\textendash} Documentation}, year = {2023}, abstract = {

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. \ 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.

}, author = {Rushrukh Rayan and Cogan Shimizu and Pascal Hitzler} } @conference {911, title = {A Modular Ontology for MODS {\textendash} Metadata Object Description Schema}, year = {2023}, abstract = {

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.\ 

}, author = {Rushrukh Rayan and Cogan Shimizu and Heidi Sieverding and Pascal Hitzler} } @article {983, title = {Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model}, journal = {IEEE Access}, volume = {11}, year = {2023}, pages = {42343-42360}, keywords = {Biological system modeling, bottom-up parsing, Convolutional Neural Network, Convolutional neural networks, Feature extraction, Location awareness, occlusion, Pose estimation, skeletal keypoint, Solid modeling}, doi = {10.1109/ACCESS.2023.3271593}, author = {Aonty, Shuhena Salam and Deb, Kaushik and Sarma, Moumita Sen and Dhar, Pranab Kumar and Shimamura, Tetsuya} } @conference {910, title = {An Ontology Design Pattern for Role-Dependent Names}, year = {2023}, abstract = {

We present an ontology design pattern for modeling\ Names\ as part of\ Roles, to capture scenarios where an\ Agent\ performs different\ Roles\ using different\ Names\ 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.

}, author = {Rushrukh Rayan and Cogan Shimizu and Pascal Hitzler} } @article {915, title = {Openness and Transparency in Academic Publishing: A Decade of Data from the Semantic Web Journal}, year = {2023}, author = {Pascal Hitzler and Krzysztof Janowicz and Cogan Shimizu and Abhilekha Dalal and Aaron Eberhart and Andrew Eells and Sanaz Saki Norouzi} } @article {965, title = {Secure Architecture for Biometric Authentication}, year = {2023}, author = {George T. Amariucai and Pascal Hitzler and Abhishek Jana and Monireh Ebrahimi and Md Kamruzzaman Sarker} } @conference {947, title = {Should I Stay or Should I Go: A New Reasoner for Description Logic}, booktitle = {Knowledge Graphs and Semantic Web {\textendash} 5th Iberoamerican Conference and 4th Indo-American Conference}, year = {2023}, author = {Aaron Eberhart and Joseph Zalewski and Pascal Hitzler} } @article {978, title = {Some Lower Bounds Related to the Missing String Problem}, journal = {Electron. Colloquium Comput. Complex.}, volume = {{TR23-027}}, year = {2023}, url = {https://eccc.weizmann.ac.il/report/2023/027}, author = {Joseph Zalewski} } @proceedings {959, title = {Sustainable Grain Transportation in Ukraine Amidst War Utilizing KNARM and KnowWhereGraph}, journal = {Proceedings of the ACM Web Conference 2023, WWW 2023}, year = {2023}, pages = {pp. 742-745}, address = {Austin, TX, USA}, author = {Yinglun Zhang and Antonina Broyaka and Jude Kasten and Allen Featherstone and Cogan Shimizu and Pascal Hitzler and Hande Kucuk McGinty} } @article {944, title = {Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge}, journal = {Journal of Web Semantics}, volume = {79:100807}, year = {2023}, author = {Cara Widmer and Md Kamruzzaman Sarker and Srikanth Nadella and Joshua Fiechter and Ion Juvina and Brandon Minnery and Pascal Hitzler and Joshua Schwartz and Michael Raymer} } @conference {949, title = {The Wikibase approach to the Enslaved.Org Hub Knowledge Graph}, booktitle = {The Semantic Web {\textendash} ISWC 2023 {\textendash} 22nd International Semantic Web Conference, Athens, Greece, November 6-10, 2023}, volume = {Proceedings, Part II. Lecture Notes in Computer Science 14266, Springer 2023}, year = {2023}, pages = {419-434}, author = {Cogan Shimizu and Pascal Hitzler and Seila Gonzalez-Estrecha and Jeff Goeke-Smith and Dean Rehberger and Catherine Foley and Alicia Sheill} } @article {894, title = {Diverse data! Diverse schemata?}, journal = {Semantic Web}, volume = {13}, year = {2022}, pages = {1{\textendash}3}, doi = {10.3233/SW-210453}, url = {https://doi.org/10.3233/SW-210453}, author = {Krzysztof Janowicz and Cogan Shimizu and Pascal Hitzler and Gengchen Mai and Shirly Stephen and Rui Zhu and Ling Cai and Lu Zhou and Mark Schildhauer and Zilong Liu and Zhangyu Wang and Meilin Shi} } @article {895, title = {{An efficient algorithm for reasoning over OWL EL ontologies with nominal schemas}}, journal = {Journal of Logic and Computation}, year = {2022}, month = {05}, abstract = {

{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.}

}, issn = {0955-792X}, doi = {10.1093/logcom/exac032}, url = {https://doi.org/10.1093/logcom/exac032}, author = {Carral, David and Zalewski, Joseph and Hitzler, Pascal} } @article {951, title = {Group D: NIEM Ontology}, year = {2022}, month = {09/2022}, pages = {31-40}, author = {Nariman Ammar and Tom Carlson and Kevin Doubleday and Katherine Escobar and Nat Fuller and Michael Grove and Brian Handspicker and Dave Hardy and Jeff Heflin and Pascal Hitzler and Sharat Israni and Eric Jahn and Ora Lassila and Timothy Lebo and Chengkai Li and Christina Medlin and Art Murray and Ron Rudnicki and Lauren Sanders and Mark Schildhauer and Greg Seaton and Cogan Shimizu and Karthik Soman and Paul Wormeli} } @article {952, title = {Group E: Connecting investments in nutrition security and climate}, year = {2022}, month = {09/2022}, pages = {41-47}, author = {Pascal Hitzler and Sudip Bhattacharjee and John MacMullen and Shashi Shekhar and Ann Stapleton and Ilya Zaslavsky and Krzysztof Janowicz and Douglas Rao and Matthew Lange and Sharat Israni and Ellie Young} } @article {977, title = {Know, Know Where, Knowwheregraph: {A} Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence}, journal = {AI Mag.}, volume = {43}, year = {2022}, pages = {30{\textendash}39}, doi = {10.1609/AIMAG.V43I1.19120}, url = {https://doi.org/10.1609/aimag.v43i1.19120}, author = {Krzysztof Janowicz and Pascal Hitzler and Wenwen Li and Dean Rehberger and Mark Schildhauer and Rui Zhu and Cogan Shimizu and Colby K. Fisher and Ling Cai and Gengchen Mai and Joseph Zalewski and Lu Zhou and Shirly Stephen and Seila Gonzalez Estrecha and Bryce D. Mecum and Anna Lopez-Carr and Andrew Schroeder and Dave Smith and Dawn J. Wright and Sizhe Wang and Yuanyuan Tian and Zilong Liu and Meilin Shi and Anthony D{\textquoteright}Onofrio and Zhining Gu and Kitty Currier} } @article {887, title = {Know, Know Where, KnowWhereGraph: A Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence}, journal = {AI Magazine}, year = {2022}, author = {Krzysztof Janowicz and Pascal Hitzler and Wenwen Li and Dean Rehberger and Mark Schildhauer and Rui Zhu and Cogan Shimizu and Colby K. Fisher and Ling Cai and Gengchen Mai and Joseph Zalewski and Lu Zhou and Shirly Stephen and Seila Gonzalez and Bryce Mecum and Anna Lopez Carr and Andrew Schroeder and Dave Smith and Dawn Wright and Sizhe Wang and Yuanyuan Tian and Zilong Liu and Meilin Shi and Anthony D{\textquoteright}Onofrio and Zhining Gu} } @conference {898, title = {LD Connect: A Linked Data Portal for IOS Press Scientometrics}, booktitle = {The Semantic Web - 19th International Conference, ESWC 2022, Hersonissos, Crete, Greece, May 29 - June 2, 2022}, year = {2022}, month = {2022}, publisher = {Springer}, organization = {Springer}, author = {Zilong Liu and Meilin Shi and Krzysztof Janowicz and Gengchen Mai and Rui Zhu and Stephanie Delbeque and Pascal Hitzler} } @proceedings {960, title = {Memory Networks for RDFS Reasoning: Experiments}, journal = {SemREC-SMART 2022, Joint Proceedings of SemREC 2022 and SMART 2022 co-located with 21st International SemanticWeb Conference (ISWC 2022)}, year = {2022}, month = {10/2022}, pages = {pp. 28-32}, publisher = {CEUR Workshop Proceedings 3337, CEUR-WS.org 2023}, address = {Hangzhou, China}, author = {Sulogna Chowdhury and Monireh Ebrahimi and Pascal Hitzler and Aaron Eberhart} } @article {946, title = {Neural Fuzzy Extractors: A Secure Way to Use Artificial Neural Networks for Biometric User Authentication}, journal = {Proceedings of Privacy Enhancing Technologies }, volume = {2022 (4)}, year = {2022}, pages = {86-104}, chapter = {86}, author = {Abhishek Jana and Bipin Paudel and Md Kamruzzaman Sarker and Monireh Ebrahimi and Pascal Hitzler and George T Amariucai} } @inbook {745, title = {Neural-Symbolic Learning and Reasoning: A Survey and Interpretation}, booktitle = {Neuro-Symbolic Artificial Intelligence: The State of the Art}, year = {2022}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, author = {Tarek R. Besold and Artur S. d{\textquoteright}Avila Garcez and Sebastian Bader and Howard Bowman and Pedro Domingos and Pascal Hitzler and Kai-Uwe K{\"u}hnberger and Lu{\'\i}s C. Lamb and Daniel Lowd and Priscila Machado Vieira Lima and Leo de Penning and Gadi Pinkas and Hoifung Poon and Gerson Zaverucha} } @article {897, title = {Neuro-symbolic approaches in artificial intelligence}, journal = {National Science Review}, volume = {9}, year = {2022}, month = {06/2022}, doi = {https://doi.org/10.1093/nsr/nwac035}, url = {https://academic.oup.com/nsr/article/9/6/nwac035/6542460}, author = {Pascal Hitzler and Aaron Eberhart and Monireh Ebrahimi and Md Kamruzzaman Sarker and Lu Zhou} } @article {862, title = {Neuro-Symbolic Artificial Intelligence: Current Trends}, journal = {AI Communications}, volume = {34}, year = {2022}, month = {2021}, chapter = {197}, author = {Md Kamruzzaman Sarker and Lu Zhou and Aaron Eberhart and Pascal Hitzler} } @book {901, title = {Neuro-Symbolic Artificial Intelligence - The State of the Art}, series = {Frontiers in Artificial Intelligence and Applications}, number = {342}, year = {2022}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, url = {https://www.iospress.com/catalog/books/neuro-symbolic-artificial-intelligence-the-state-of-the-art}, author = {Pascal Hitzler and Md Kamruzzaman Sarker} } @article {950, title = {Open Knowledge Network Roadmap: Powering The Next Data Revolution}, year = {2022}, author = {Chaitan Baru and Lara Campbell and Wo Chang and Tess DeBlank-Knowles and Jemin George and Martin Halbert and Kat Albrecht and Luis Amaral and Nariman Ammar and Todd Bacastow and Sergio Baranzini and Matt Bishop and Michael Cafarella and Silviu Cucerzan and Ying Ding and Brian Handspicker and Oktie Hassanzadeh and Pascal Hitzler and Florence Hudson and Sharat Israni and Angela Rizk-Jackson and Esther Jackson and Eric Jahn and Krzysztof Janowicz and Bandana Kar and Sam Klein and Matthew Lange and Ora Lassila and Chengkai Li and Ryan McGranaghan and Murat Omay and Adam Pah and Louiqa Raschid and Glenn Ricart and Emanuel Sallinger and Greg Seaton and Cogan Shimizu and Amanda Stahopoulos and Paul Wormeli and Lilit Yeghiazarian and Ellie Young and Pamela Livingston and Douglas Maughan and Shelby Smith} } @article {953, title = {Open Knowledge Network Roadmap:Appendix B}, year = {2022}, month = {09/2022}, author = {Pascal Hitzler} } @proceedings {962, title = {A Pattern for Modeling Computational Observations}, journal = {Proceedings of the 13th Workshop on Ontology Design and Patterns, WOP2022, at the 21st International Semantic Web Conference, ISWC 2022}, year = {2022}, month = {10/2022}, author = {Cogan Shimizu and Pascal Hitzler and Charles Vardeman II} } @proceedings {961, title = {A Pattern for Representing Scientific Taxonomies}, journal = {Proceedings of the 13th Workshop on Ontology Design and Patterns, WOP2022, at the 21st International Semantic Web Conference, ISWC 2022}, year = {2022}, month = {10/2022}, author = {Shirly Stephen and Cogan Shimizu and Mark Schildhauer and Rui Zhu and Krzysztof Janowicz and Pascal Hitzler} } @proceedings {963, title = {Some advances regarding ontologies and neuro-symbolic artificial intelligence}, journal = {Proceedings of the Workshop on Meta-Knowledge Transfer/ Communication in Different Systems at ECML/PKDD 2022}, year = {2022}, author = {Pascal Hitzler} } @article {863, title = {Advancing Agriculture through Semantic Data Management}, journal = {Semantic Web}, volume = {12}, year = {2021}, type = {Editorial}, chapter = {1}, author = {Pascal Hitzler and Krzysztof Janowicz and Ajay Sharda and Cogan Shimizu} } @proceedings {871, title = {Aligning Patterns to the Wikibase Model}, journal = {Workshop on Ontology Design and Patterns}, year = {2021}, author = {Andrew Eells and Cogan Shimizu and Lu Zhou and Pascal Hitzler and Seila Gonzalez Estrecha and Dean Rehberger} } @conference {880, title = {Automatically Generating Human Readable Documentation for Ontology Design Patterns}, booktitle = {International Semantic Web Conference Poster and Demos}, year = {2021}, author = {Cogan Shimizu and Pascal Hitzler} } @article {968, title = {Automatically Generating Human Readable Documentation for Ontology Design Patterns}, year = {2021}, author = {Cogan Shimizu and Pascal Hitzler} } @conference {982, title = {BMGC: A Deep Learning Approach to Classify Bengali Music Genres}, booktitle = {Proceedings of the 4th International Conference on Networking, Information Systems \& Security}, year = {2021}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {

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: {\textquoteleft}Adhunik{\textquoteright}, {\textquoteleft}Band{\textquoteright}, {\textquoteleft}Hiphop{\textquoteright}, {\textquoteleft}Nazrulgeeti{\textquoteright}, {\textquoteleft}Lalon{\textquoteright}, and {\textquoteleft}Rabindra Sangeet{\textquoteright}. 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.

}, keywords = {Bengali music classification, Bengali Music Dataset., Deep Learning, Gated Recurrent Unit (GRU), Mel Frequency Cepstral Coefficient (MFCC), Recurrent Neural Network}, isbn = {9781450388719}, doi = {10.1145/3454127.3456593}, url = {https://doi.org/10.1145/3454127.3456593}, author = {Sen Sarma, Moumita and Das, Avishek} } @conference {867, title = {Bridging Upper Ontology and Modular Ontology Modeling: A Tool and Evaluation}, booktitle = {KGSWC-2021}, year = {2021}, abstract = {

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{\textquoteright}ge{\textquoteright}\ 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{\textquoteright}ge{\textquoteright} 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.

}, author = {Abhilekha Dalal and Cogan Shimizu and Pascal Hitzler} } @article {865, title = {On the Capabilities of Pointer Networks for Deep Deductive Reasoning}, journal = {arXiv}, year = {2021}, author = {Monireh Ebrahimi and Aaron Eberhart and Pascal Hitzler} } @conference {869, title = {Environmental Observations in Knowledge Graphs}, booktitle = {DaMaLOS 2021 @ ISWC}, year = {2021}, abstract = {

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.\ 

}, author = {Rui Zhu and Shirly Stephen Ambrose and Lu Zhou and Cogan Shimizu and Ling Cai and Gengchen Mai and Krzysztof Janowicz and Pascal Hitzler and Mark Schildhauer} } @conference {860, title = {Expressibility of OWL Axioms with Patterns}, booktitle = {ESWC 2021}, year = {2021}, abstract = {

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 Prot{\'e}g{\'e} 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.

}, author = {Aaron Eberhart and Cogan Shimizu and Sulogna Chowdhury and Md Kamruzzaman Sarker and Pascal Hitzler} } @conference {948, title = {Expressibility of OWL Axioms with Patterns}, booktitle = {The Semantic Web {\textendash} 18th International Conference, ESWC 2021}, year = {2021}, pages = {230-245}, author = {Aaron Eberhart and Cogan M Shimizu and Sulogna Chowdhury and Md Kamruzzaman Sarker and Pascal Hitzler} } @conference {877, title = {InK Browser - The Interactive Knowledge Browser}, booktitle = {International Semantic Web Conference}, volume = {20}, year = {2021}, abstract = {

We present an improved implementation of the Interactive Knowledge Browser (InK Browser), a tool for exploring knowledge graphs visually, using a schema diagram.

}, author = {Joseph Zalewski and Lu Zhou and Cogan Shimizu and Pascal Hitzler} } @article {861, title = {Modular Ontology Modeling}, journal = {Semantic Web}, year = {2021}, author = {Cogan Shimizu and Karl Hammar and Pascal Hitzler} } @conference {856, title = {Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment}, booktitle = {AAAI-MAKE 2021}, year = {2021}, publisher = {AAAI}, organization = {AAAI}, author = {Monireh Ebrahimi and Md Kamruzzaman Sarker and Federico Bianchi and Ning Xie and Aaron Eberhart and Derek Doran and HyeongSik Kim and Pascal Hitzler} } @article {858, title = {Open Science data and the Semantic Web journal}, journal = {Semantic Web}, volume = {12(3)}, year = {2021}, month = {2021}, type = {Editorial}, chapter = {401-402}, doi = {10.3233/SW-210427}, author = {Pascal Hitzler and Krzysztof Janowicz and Cogan Shimizu and Lu Zhou and Andrew Eells} } @conference {900, title = {A Pattern for Features on a Hierarchical Spatial Grid}, booktitle = {The 10th International Joint Conference on Knowledge Graphs, IJCKG 2021, December 6-8, 2021, Virtual Event, Thailand}, year = {2021}, publisher = {ACM}, organization = {ACM}, author = {Cogan Shimizu and Rui Zhu and Gengchen Mai and Mark Schildhauer and Krzysztof Janowicz and Pascal Hitzler} } @conference {881, title = {A Pattern for Modeling Causal Relations Between Events}, booktitle = {13th Workshop on Ontology Design and Patterns}, year = {2021}, author = {Cogan Shimizu and Rui Zhu and Genchen Mai and Mark Schildhauer and Krzysztof Janowicz and Pascal Hitzler} } @proceedings {964, title = {Results of the Ontology Alignment Evaluation Initiative 2021}, journal = {Proceedings of the 16th International Workshop on Ontology Matching co-located with the 20th International Semantic Web Conference (ISWC 2021)}, year = {2021}, month = {10/2021}, author = {Mina Abd Nikooie Pour and Alsayed Algergawy and Florence Amardeilh and Reihaneh Amini and Omaima Fallatah and Daniel Faria and Irini Fundulaki and Ian Harrow and Sven Hertling and Pascal Hitzler and Martin Huschka and Liliana Ibanescu and Ernesto Jimenez- Ruiz and Naouel Karam and Amir Laadhar and Patrick Lambrix and Huanyu Li and Ying Li and Franck Michel and Engy Nasr and Heiko Paulheim and Catia Pesquita and Jan Portisch and Catherine Roussey and Tzanina Saveta and Pavel Shvaiko and Andrea Splendiani and Cassia Trojahn and Jana Vatascinova and Beyza Yaman and Ondrej Zamazal and Lu Zhou} } @article {945, title = {A Review of the Semantic Web Field}, journal = {Communications of the ACM}, volume = {64}, year = {2021}, month = {02/2021}, pages = {76-83}, chapter = {76}, author = {Pascal Hitzler} } @inbook {814, title = {Seed Patterns for Modeling Trees}, booktitle = {Advances in Pattern-Based Ontology Engineering}, year = {2021}, pages = {48-67}, publisher = {IOS Press}, organization = {IOS Press}, abstract = {Trees {\textendash} i.e., the type of data structure known under this name {\textendash} 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.}, author = {Aaron Eberhart and David Carral and Pascal Hitzler and Hilmar Lapp and Sebastian Rudolph} } @conference {888, title = {Semantic Compression with Region Calculi in Nested Hierarchical Grids}, booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems}, year = {2021}, pages = {305{\textendash}308}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {

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.

}, keywords = {Hierarchical Grids, Knowledge Graphs, RCC5}, isbn = {9781450386647}, doi = {10.1145/3474717.3483965}, url = {https://doi.org/10.1145/3474717.3483965}, author = {Zalewski, Joseph and Hitzler, Pascal and Janowicz, Krzysztof} } @article {864, title = {Semantic Compression with Region Calculi in Nested Hierarchical Grids (Technical Report)}, year = {2021}, abstract = {

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.

}, keywords = {Hierarchical Grids, Knowledge Graphs, RCC5}, author = {Joseph Zalewski and Pascal Hitzler and Krzysztof Janowicz} } @conference {899, title = {SOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology}, booktitle = {The 10th International Joint Conference on Knowledge Graphs, IJCKG 2021, December 6-8, 2021, Virtual Event, Thailand}, year = {2021}, publisher = {ACM}, organization = {ACM}, author = {Rui Zhu and Cogan Shimizu and Shirly Stephen and Lu Zhou and Ling Cai and Gengchen Mai and Krzysztof Janowicz and Mark Schildhauer and Pascal Hitzler} } @proceedings {870, title = {Toward Undifferentiated Cognitive Models}, journal = {International Conference on Cognitive Modeling}, year = {2021}, edition = {19}, abstract = {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{\textquoteright} 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.}, author = {Colin Kupitz and Aaron Eberhart and Daniel Schmidt and Christopher Stevens and Cogan Shimizu and Pascal Hitzler and Dario Salvucci and Benji Maruyama and Chris Myers} } @inbook {826, title = {Towards a Modular Ontology for Space Weather Research}, booktitle = {Advances in Ontology Design Patterns}, year = {2021}, publisher = {IOS Press}, organization = {IOS Press}, author = {Cogan Shimizu and Ryan McGranaghan and Aaron Eberhart and Adam C. Kellerman} } @article {855, title = {Towards Bridging the Neuro-Symbolic Gap: Deep Deductive Reasoners}, journal = {Applied Intelligence}, year = {2021}, author = {Monireh Ebrahimi and Aaron Eberhart and Federico Bianchi and Pascal Hitzler} } @mastersthesis {878, title = {Towards generalizable neuro-symbolic reasoners}, volume = {PhD}, year = {2021}, month = {08/2021}, school = {Kansas State University}, type = {Dissertation}, address = {Manhattan, KS}, url = {https://krex.k-state.edu/dspace/handle/2097/41621}, author = {Monireh Ebrahimi} } @article {980, title = {Traditional Bangladeshi Sports Video Classification Using Deep Learning Method}, journal = {Applied Sciences}, volume = {11}, year = {2021}, abstract = {

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.

}, issn = {2076-3417}, doi = {10.3390/app11052149}, url = {https://www.mdpi.com/2076-3417/11/5/2149}, author = {Sarma, Moumita Sen and Deb, Kaushik and Dhar, Pranab Kumar and Koshiba, Takeshi} } @conference {981, title = {Traditional Bengali Food Classification Using Convolutional Neural Network}, booktitle = {2021 IEEE Region 10 Symposium (TENSYMP)}, year = {2021}, pages = {1-8}, keywords = {Computational modeling, Convolutional Neural Network, Convolutional neural networks, Data models, fine tuning, image classification, Image recognition, Neural networks, traditional Bengali foods, Transfer learning, Turning, VGG16}, doi = {10.1109/TENSYMP52854.2021.9550874}, author = {Uddin, Asif Mahbub and Al Miraj, Abdullah and Sen Sarma, Moumita and Das, Avishek and Gani, Md. Manjurul} } @mastersthesis {807, title = {Advances in modular ontology engineering: methodology and infrastructure}, volume = {Ph.D.}, year = {2020}, month = {08/2020}, pages = {175}, school = {Kansas State University}, type = {Cumulative}, address = {Manhattan}, abstract = {

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.

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.

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.

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.\ 

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.

Altogether, these research topics, resulted in a methodology, that when executed, produced actually reusable, extendable, and adaptable ontologies.\ 

}, author = {Cogan Shimizu} } @proceedings {852, title = {AROA Results of OAEI 2020}, journal = {Ontology Matching}, year = {2020}, month = {12/2020}, publisher = {Springer}, author = {Lu Zhou and Pascal Hitzler} } @proceedings {770, title = {Completion Reasoning Emulation for the Description Logic EL+}, journal = {Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice}, volume = {2600}, year = {2020}, month = {03/2020}, publisher = {CEUR-WS.org}, address = {Stanford University, Palo Alto, California, USA}, abstract = {

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{\textquoteright}s and LSTM{\textquoteright}s predictions on corrupt data with correct answers.

}, keywords = {Deep Learning, Description Logic, EL+, LSTM, NeSy, Reasoning}, url = {http://ceur-ws.org/Vol-2600/paper5.pdf}, author = {Aaron Eberhart and Monireh Ebrahimi and Lu Zhou and Cogan Shimizu and Pascal Hitzler} } @article {853, title = {Counterfactual reasoning over large-scale human performance optimization experiments}, journal = {Virtual poster presented at the annual meeting of the Psychonomic Society, November 2020}, year = {2020}, author = {Ion Juvina and William R. Aue and Brandon Minnery and Pascal Hitzler and Srikanth Nadella and Md Kamruzzaman Sarker} } @conference {851, title = {CSSA{\textquoteright}20: Workshop on Combining Symbolic and Sub-Symbolic Methods and their Applications}, booktitle = {CIKM{\textquoteright}20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, October 19-23, 2020}, year = {2020}, pages = {3523-3524}, publisher = {ACM}, organization = {ACM}, author = {Mehwish Alam and Paul Groth and Pascal Hitzler and Heiko Paulheim and Harald Sack and Volker Tresp} } @article {825, title = {Data Integration with Knowledge Graphs: A Space Weather Use-case}, year = {2020}, address = {American Geophysical Union (AGU) Fall Meeting 2020}, author = {Cogan Shimizu and Ryan McGranaghan and Adam C. Kellerman} } @conference {819, title = {A Domain Ontology for Task Instructions}, booktitle = {KGSWC}, year = {2020}, abstract = { Knowledge graphs and ontologies represent information in a variety of different applications. One use case, the Intelligence, Surveillance, \& 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{\textquoteright}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.}, author = {Aaron Eberhart and Cogan Shimizu and Christopher Stevens and Pascal Hitzler and Christopher W. Myers and Benji Maruyam} } @conference {788, title = {The Enslaved Dataset: A Real-world Complex Ontology Alignment Benchmark using Wikibase}, booktitle = {29th ACM International Conference on Information and Knowledge Management}, year = {2020}, month = {10/2020}, publisher = {ACM}, organization = {ACM}, author = {Lu Zhou and Cogan Shimizu and Pascal Hitzler and Alicia M Sheill and Seila Gonzalez Estrecha and Catherine Foley and Duncan Tarr and Dean Rehberger} } @article {784, title = {The Enslaved Ontology: Peoples of the Historic Slave Trade}, journal = {Journal of Web Semantics}, volume = {63}, year = {2020}, month = {08/2020}, abstract = {

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.

}, keywords = {data integration, digital humanities, history of the slave trade, modular ontology, Ontology Design Patterns}, doi = {https://doi.org/10.1016/j.websem.2020.100567}, author = {Cogan Shimizu and Pascal Hitzler and Quinn Hirt and Dean Rehberger and Seila Gonzalez Estrecha and Catherine Foley and Alicia M. Sheill and Walter Hawthorne and Jeff Mixter and Ethan Watrall and Ryan Carty and Duncan Tarr} } @conference {792, title = {A Framework for Explainable Deep Neural Models Using External Knowledge Graphs}, booktitle = {Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, SPIE}, year = {2020}, author = {Zachary A. Daniels and Logan D. Frank and Christopher J. Menart and Michael Raymer and Pascal Hitzler} } @article {811, title = {A Functional API for OWL}, journal = {The 19th International Semantic Web Conference}, volume = {2721}, year = {2020}, abstract = {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!}, author = {Aaron Eberhart and Pascal Hitzler} } @conference {789, title = {GeoLink Cruises: A Non-Synthetic Benchmark for Co-Reference Resolution on Knowledge Graphs}, booktitle = {International conference on information and knowledge management}, year = {2020}, month = {10/2020}, publisher = {ACM DL}, organization = {ACM DL}, author = {Reihaneh Amini and Lu Zhou and Pascal Hitzler} } @article {755, title = {GeoLink Dataset: A Complex Alignment Benchmark from Real-world Ontology}, journal = {Data Intelligence }, year = {2020}, author = {Lu Zhou and Michelle Cheatham and Adila Krisnadhi and Pascal Hitzler} } @article {791, title = {Gold-Level Open Access at the Semantic Web Journal}, journal = {Semantic Web}, volume = {11}, year = {2020}, url = {http://www.semantic-web-journal.net/content/gold-level-open-access-semantic-web-journal}, author = {Krzysztof Janowicz and Pascal Hitzler} } @book {795, title = {Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges}, series = {Studies on the Semantic Web}, volume = {47}, year = {2020}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, url = {https://www.iospress.nl/book/knowledge-graphs-for-explainable-artificial-intelligence-foundations-applications-and-challenges/}, author = {Ilaria Tiddi and Freddy L{\'e}cu{\'e} and Pascal Hitzler} } @conference {806, title = {Modular Graphical Ontology Engineering Evaluated}, booktitle = {The Semantic Web - 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, 2020, Proceedings}, volume = {12123}, year = {2020}, pages = {20{\textendash}35}, publisher = {Springer}, organization = {Springer}, doi = {10.1007/978-3-030-49461-2\2}, url = {https://doi.org/10.1007/978-3-030-49461-2\2}, author = {Cogan Shimizu and Karl Hammar and Pascal Hitzler}, editor = {Andreas Harth and Sabrina Kirrane and Axel-Cyrille Ngonga Ngomo and Heiko Paulheim and Anisa Rula and Anna Lisa Gentile and Peter Haase and Michael Cochez} } @inbook {802, title = {Modular Ontology Modeling: A Tutorial}, booktitle = {Applications and Practices in Ontology Design, Extraction, and Reasoning}, volume = {49}, year = {2020}, publisher = {IOS Press}, organization = {IOS Press}, chapter = {1}, abstract = {

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
the reader to have an understanding of the underlying motivation and methodology for producing a modular ontology.

}, author = {Cogan Shimizu and Pascal Hitzler and Adila Krisnadhi} } @conference {816, title = {Modular Ontology Modeling Meets Upper Ontologies: The Upper Ontology Alignment Tool}, booktitle = {The 19th International Semantic Web Conference}, volume = {2721}, year = {2020}, month = {10/2020}, pages = {119-124}, abstract = {

We provide an extension to the Prote{\textquoteright}ge{\textquoteright}-based modular\ 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.

}, author = {Abhilekha Dalal and Cogan Shimizu and Pascal Hitzler} } @mastersthesis {859, title = {Modular Ontology Modeling Meets Upper Ontologies: The Upper Ontology Alignment Tool}, volume = {Masters}, year = {2020}, month = {11/2020}, pages = {35}, school = {Kansas State University}, address = {Manhattan}, abstract = {

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.

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{\textquoteright} 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).

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 Prot{\'e}g{\'e} - 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.\ 

}, author = {Abhilekha Dalal} } @article {775, title = {Multimodal mental health analysis in social media}, journal = {PLoS ONE}, year = {2020}, abstract = {

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}

Depression is a major public health concern in the U.S. and globally. While successful early

identification and treatment can lead to many positive health and behavioral outcomes,

depression, remains undiagnosed, untreated or undertreated due to several reasons,

including denial of the illness as well as cultural and social stigma. With the ubiquity of social

media platforms, millions of people are now sharing their online persona by expressing their

thoughts, moods, emotions, and even their daily struggles with mental health on social

media. Unlike traditional observational cohort studies conducted through questionnaires

and self-reported surveys, we explore the reliable detection of depressive symptoms from

tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)

data to discern depressive behaviors using a wide variety of features including individuallevel

demographics. By developing a multimodal framework and employing statistical techniques

to fuse heterogeneous sets of features obtained through the processing of visual,

textual, and user interaction data, we significantly enhance the current state-of-the-art

approaches for identifying depressed individuals on Twitter (improving the average F1-

Score by 5 percent) as well as facilitate demographic inferences from social media. Besides

providing insights into the relationship between demographics and mental health, our

research assists in the design of a new breed of demographic-aware health interventions.

}, keywords = {Explainable Machine Learning, Hypothesis Testing, National Language Processing, Prediction, Regression}, url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248\&type=printable}, author = {Amir Hossein Yazdavar and Mohammad Saeid Mahdavinejad and Goonmeet Baja and William Romine and Amit Sheth and Amir Hassan Monadjemi and Krishnaprasad Thirunarayan and John M. Meddar and Annie Myers and Jyotishman Pathak and Pascal Hitzler} } @article {790, title = {Neural-Symbolic Integration and the Semantic Web}, journal = {Semantic Web}, volume = {11}, year = {2020}, url = {http://www.semantic-web-journal.net/content/neural-symbolic-integration-and-semantic-web-0}, author = {Pascal Hitzler and Federico Bianchi and Monireh Ebrahimi and Md Kamruzzaman Sarker} } @article {823, title = {An Ontology of Instruction 1.0}, year = {2020}, author = {Cogan Shimizu and Pascal Hitzler and Aaron Eberhart and Quinn Hirt and Christopher Stevens and Christopher W. Myers and Benji Maruyama and Colin Kupitz and Dario Salvucci} } @proceedings {854, title = {Results of theOntology Alignment Evaluation Initiative 2020}, journal = {15th International Workshop on Ontology Matching}, year = {2020}, author = {Mina Abd Nikooie Pour and Alsayed Algergawy and Reihaneh Amini and Daniel Faria and Irini Fundulaki and Ian Harrow and Sven Hertling and Ernesto Jim{\'e}nez-Ruiz and Clement Jonquet and Naouel Karam and Abderrahmane Khiat and Amir Laadhar and Patrick Lambrix and Huanyu Li and Ying Li and Pascal Hitzler and Heiko Paulheim and Catia Pesquita and Tzanina Saveta and Pavel Shvaiko and Andrea Splendiani and Elodie Thieblin and Cassia Trojahn and Jana Vatascinova and Beyza Yaman and Ondrej Zamazal and Lu Zhou} } @article {805, title = { A Review Of The Semantic Web Field}, journal = {Communications of the ACM}, year = {2020}, abstract = {

We review two decades of Semantic Web research and applications, discuss relationships to some other disciplines, and current challenges in the field.

}, author = {Pascal Hitzler} } @article {820, title = {Time is ripe to embrace the scientific approach in Applied Ontology}, journal = {Appl. Ontology}, volume = {15}, year = {2020}, pages = {245{\textendash}249}, doi = {10.3233/AO-200237}, url = {https://doi.org/10.3233/AO-200237}, author = {Stefano Borgo and Pascal Hitzler and Cogan Shimizu} } @article {821, title = {Towards a Modular Ontology for Space Weather Research}, journal = {CoRR}, volume = {abs/2009.12285}, year = {2020}, url = {https://arxiv.org/abs/2009.12285}, author = {Cogan Shimizu and Ryan McGranaghan and Aaron Eberhart and Adam C. Kellerman} } @mastersthesis {879, title = {Towards automated complex ontology alignment using rule-based machine learning}, volume = {Doctorate}, year = {2020}, school = {Kansas State University}, type = {Dissertation}, address = {Manhattan}, author = {Lu Zhou} } @article {768, title = {Towards Evaluating Complex Ontology Alignments}, journal = {Knowledge Engineering Review}, volume = {35}, year = {2020}, chapter = {e21}, author = {Lu Zhou and Elodie Thieblin and Michelle Cheatham and Daniel Faria and Catia Pesquita and Cassia Trojahn and Ondrej Zamazal} } @conference {813, title = {Wikipedia Knowledge Graph for Explainable AI}, booktitle = {Second Iberoamerican Knowledge Graphs and Semantic Web Conference (KGSWC)}, year = {2020}, month = {11/2020}, abstract = {

Explainable artificial intelligence (XAI) requires domain information to explain a system{\textquoteright}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.

}, author = {Md Kamruzzaman Sarker and Joshua Schwartz and Pascal Hitzler and Lu Zhou and Srikanth Nadella and Brandon Minnery and Ion Juvina and Michael L. Raymer and William R. Aue} } @article {763, title = {Alignment of Surface Water Ontologies: A comparison of manual and automated approaches}, journal = {Journal of Geographical Systems}, year = {2019}, doi = {10.1007/s10109-019-00312-3}, author = {Michelle Cheatham and Dalia Varanka and Fatima Arauz and Lu Zhou} } @booklet {943, title = {Alignment Rules from GBO to GMO}, year = {2019}, author = {Lu Zhou} } @conference {769, title = {AROA Results of 2019 OAEI}, booktitle = {Ontology Matching}, year = {2019}, publisher = {CEUR}, organization = {CEUR}, address = {Auckland, New Zealand}, author = {Lu Zhou and Michelle Cheatham and Pascal Hitzler} } @conference {741, title = {On the Capabilities of Logic Tensor Networks for Deductive Reasoning}, booktitle = {AAAI Spring Symposium 2019}, year = {2019}, author = {Bianchi, Federico and Pascal Hitzler} } @article {720, title = {A closer look at the Semantic Web journal{\textquoteright}s review process}, year = {2019}, doi = {10.3233/SW-180342}, url = {https://doi.org/10.3233/SW-180342}, author = {Cogan Shimizu and Pascal Hitzler and Krzysztof Janowicz} } @conference {761, title = {CoModIDE - The Comprehensive Modular Ontology IDE}, booktitle = {18th International Semantic Web Conference: Satellite Events}, year = {2019}, author = {Cogan Shimizu and Karl Hammar} } @conference {793, title = {Complementing Logical Reasoning with Sub-symbolic Commonsense}, booktitle = {Rules and Reasoning - Third International Joint Conference, RuleML+RR 2019, Bolzano, Italy, September 16-19, 2019}, year = {2019}, publisher = {Springer}, organization = {Springer}, author = {Federico Bianchi and Matteo Palmonari and Pascal Hitzler and Luciano Serafini} } @conference {772, title = {Constrained State-Preserved Extreme Learning Machine}, booktitle = {31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019, Portland, OR, USA, November 4-6, 2019}, year = {2019}, pages = {752{\textendash}759}, publisher = {IEEE}, organization = {IEEE}, doi = {10.1109/ICTAI.2019.00109}, url = {https://doi.org/10.1109/ICTAI.2019.00109}, author = {Garrett Goodman and Cogan Shimizu and Iosif Papadakis Ktistakis} } @conference {713, title = {Efficient Concept Induction for Description Logics}, booktitle = {AAAI Conference on Artificial Intelligence}, volume = {33}, year = {2019}, month = {01/2019}, publisher = {AAAI}, organization = {AAAI}, address = {Honolulu, US}, abstract = {

Concept Induction refers to the problem of creating complex Description Logic class descriptions (i.e., TBox axioms) from instance examples (i.e.,\  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.

}, author = {Md Kamruzzaman Sarker and Pascal Hitzler} } @conference {756, title = {Exploring the Impact of Training Data Bias on Automatic Generation of Video Captions}, booktitle = {MultiMedia Modeling}, year = {2019}, pages = {178{\textendash}190}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

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.

}, isbn = {978-3-030-05710-7}, author = {Smeaton, Alan F. and Graham, Yvette and McGuinness, Kevin and O{\textquoteright}Connor, Noel E. and Quinn, Se{\'a}n and Arazo Sanchez, Eric} } @conference {773, title = {Extensions to the Ontology Design Pattern Representation Language}, booktitle = {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}, volume = {2459}, year = {2019}, pages = {76{\textendash}75}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, url = {http://ceur-ws.org/Vol-2459/short2.pdf}, author = {Quinn Hirt and Cogan Shimizu and Pascal Hitzler} } @conference {747, title = {A Method for Automatically Generating Schema Diagrams for OWL Ontologies}, booktitle = {1st Iberoamerican Knowledge Graph and Semantic Web Conference (KGSWC)}, year = {2019}, month = {06/2019}, publisher = {Springer}, organization = {Springer}, chapter = {149-161}, address = {Villa Clara, Cuba}, abstract = {

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.

}, keywords = {design patterns, evaluation, implementation, ontology, schema diagrams, visualization}, author = {Cogan Shimizu and Aaron Eberhart and Nazifa Karima and Quinn Hirt and Adila Krisnadhi and Pascal Hitzler} } @proceedings {760, title = {MODL: a Modular Ontology Design Library}, journal = {Workshop on Ontology Design and Patterns}, year = {2019}, author = {Cogan Shimizu and Quinn Hirt and Pascal Hitzler} } @book {796, title = {The Semantic Web. 16th International Conference, ESWC 2019, Portoroz, Slovenia, June 2-6, 2019, Proceedings}, series = {Lecture Notes in Computer Science}, volume = {11503}, year = {2019}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg}, author = {Pascal Hitzler and Miriam Fernandez and Krzysztof Janowicz and Amrapali Zaveri and Alasdair Gray and Vanessa Lopez and Armin Haller and Karl Hammar} } @book {797, title = {The Semantic Web: ESWC 2019 Satellite Events. Portoroz, Slovenia, June 2-6, 2019, Revised Selected Papers}, series = {Lecture Notes In Computer Science}, volume = {11762}, year = {2019}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg}, author = {Pascal Hitzler and Sabrina Kirrane and Olaf Hartig and Victor de Boer and Maria-Esther Vidal and Maria Maleshkova and Stefan Schlobach and Karl Hammar and Nelia Lasierra and Steffen Stadtm{\"u}ller and Katja Hose and Ruben Verborgh} } @conference {757, title = {Towards Architecture-Agnostic Neural Transfer: a Knowledge-Enhanced Approach}, booktitle = {Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI-19}}, year = {2019}, month = {7}, pages = {6452{\textendash}6453}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, organization = {International Joint Conferences on Artificial Intelligence Organization}, doi = {10.24963/ijcai.2019/915}, url = {https://doi.org/10.24963/ijcai.2019/915}, author = {Quinn, Se{\'a}n and Mileo, Alessandra} } @conference {767, title = {Towards Association Rule-Based Complex Ontology Alignment}, booktitle = {Joint International Semantic Technology Conference}, year = {2019}, publisher = {Springer}, organization = {Springer}, address = {Hangzhou, China}, author = {Lu Zhou and Michelle Cheatham and Pascal Hitzler} } @conference {758, title = {Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption}, booktitle = {2nd International Conference on Sensors, Signal and Image Processing (SSIP 19)}, year = {2019}, publisher = {ACM}, organization = {ACM}, address = {Prague, Czech Republic}, abstract = {

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\  early illness\  detection\  and\  for\  monitoring\  the well-being of those with known illnesses.

}, keywords = {Ambient Assisted Living, Home Monitoring, Internet of Things, Sensor Applications, Sensor Networks}, author = {Quinn, Se{\'a}n and Murphy, Noel and Smeaton, Alan F.} } @article {703, title = {Caregiver Assessment Using Smart Gaming Technology: {A} Preliminary Approach}, journal = {CoRR}, volume = {abs/1802.03051}, year = {2018}, url = {http://arxiv.org/abs/1802.03051}, author = {Garrett Goodman and Abby Edwards and Cogan Shimizu and Tanvi Banerjee and Jennifer Hughes and William Romine and Larry Lawhorne} } @conference {694, title = {A Complex Alignment Benchmark: Geolink dataset}, booktitle = {ISWC}, year = {2018}, publisher = {Springer}, organization = {Springer}, author = {Lu Zhou and Michelle Cheatham and Adila Krisnadhi and Pascal Hitzler} } @article {693, title = {The First Version of the OAEI Complex Alignment Benchmark}, journal = {ISWC Poster and Demo Session}, year = {2018}, author = {Elodie Thieblin and Michelle Cheatham and Cassia Trojahn and Ondrej Zamazal and Lu Zhou} } @proceedings {742, title = {Formal Ontology in Information Systems - Proceedings of the 10th International Conference, FOIS 2018, Cape Town, South Africa, 19-21 September 2018}, journal = {FOIS 2018}, year = {2018}, publisher = {IOS Press}, author = {Stefano Borgo and Pascal Hitzler and Oliver Kutz} } @article {715, title = {The GeoLink Knowledge Graph}, journal = {Big Earth Data}, year = {2018}, author = {Michelle Cheatham and Adila Krisnadhi and Reihaneh Amini and Pascal Hitzler and Krzysztof Janowicz and Adam Shepherd and Tom Narock and Matt Jones and Peng Ji} } @article {691, title = {The GeoLink Knowledge Graph}, journal = {Big Earth Data}, year = {2018}, author = {Michelle Cheatham and Adila Krisnadhi and Reihaneh Amini and Pascal Hitzler and Krzysztof Janowicz and Adam Shepherd and Tom Narock and Matt Jones and Peng Ji} } @article {692, title = {Intelligent Systems for Geosciences - An Essential Research Agenda}, journal = {Communications of the ACM}, year = {2018}, author = {Yolanda Gil and Suzanne Pierce and Hassan Babaie and Arindam Banerjee and Kirk Borne and Gary Bust and Michelle Cheatham and Imme Ebert-Uphoff and Carla Gomes and Mary Hill and John Horel and Leslie Hsu and Jim Kinter and Craig Knoblock and David Krum and Vipin Kumar and Pierre Lermusiaux and Yan Liu and Chris North and Victor Pankratius and Shanan Peters and Beth Plale and Allen Pope and Sai Ravela and Juan Restrepo and Aaron Ridley and Hanan Samet and Shashi Shekhar and Katie Skinner and Padhraic Smyth and Basil Tikoff and Lynn Yarmey and Jia Zhang} } @conference {695, title = {A Journey From Simple to Complex Alignment on Real-World Ontologies}, booktitle = {ISWC}, year = {2018}, author = {Lu Zhou} } @article {890, title = {Machine learning for internet of things data analysis: a survey}, journal = {Digital Communications and Networks}, volume = {4}, year = {2018}, pages = {161-175}, abstract = {

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.

}, keywords = {Internet of Things, Machine learning, Smart City, Smart data}, issn = {2352-8648}, doi = {https://doi.org/10.1016/j.dcan.2017.10.002}, url = {https://www.sciencedirect.com/science/article/pii/S235286481730247X}, author = {Mohammad Saeid Mahdavinejad and Mohammadreza Rezvan}, editor = {Mohammadamin Barekatain and Peyman Adibi and Payam Barnaghi and Amit P. Sheth} } @conference {891, title = {Mental Health Analysis Via Social Media Data}, booktitle = {2018 IEEE International Conference on Healthcare Informatics (ICHI)}, year = {2018}, pages = {459-460}, doi = {10.1109/ICHI.2018.00102}, author = {Yazdavar, Amir Hossein and Mahdavinejad, Mohammad Saied and Bajaj, Goonmeet and Thirunarayan, Krishnaprasad and Pathak, Jyotishman and Sheth, Amit} } @conference {777, title = {Mental Health Analysis Via Social Media Data, IEEE ICHI 2018}, booktitle = {IEEE, ICHI}, year = {2018}, author = {Amir Hossein Yazdavar and Mohammad Saied Mahdavinejad and Goonmeet Bajaj and Krishnaprasad Thirunarayan and Jyotishman Pathak and Amit Sheth} } @conference {707, title = {Modular Ontologies as a Bridge Between Human Conceptualization and Data}, booktitle = {Graph-Based Representation and Reasoning - 23rd International Conference on Conceptual Structures, ICCS 2018, Edinburgh, UK, June 20-22, 2018, Proceedings}, volume = {10872}, year = {2018}, pages = {3{\textendash}6}, publisher = {Springer}, organization = {Springer}, doi = {10.1007/978-3-319-91379-7\_1}, url = {https://doi.org/10.1007/978-3-319-91379-7\_1}, author = {Pascal Hitzler and Cogan Shimizu} } @conference {704, title = {Ontology Design Patterns for Winston{\textquoteright}s Taxonomy Of Part-Whole Relations}, booktitle = {Emerging Topics in Semantic Technologies - ISWC 2018 Satellite Events [best papers from 13 of the workshops co-located with the ISWC 2018 conference]}, volume = {36}, year = {2018}, pages = {119{\textendash}129}, publisher = {IOS Press}, organization = {IOS Press}, doi = {10.3233/978-1-61499-894-5-119}, url = {https://doi.org/10.3233/978-1-61499-894-5-119}, author = {Cogan Shimizu and Pascal Hitzler and Clare Paul} } @article {714, title = {The Properties of Property Alignment on the Semantic Web}, journal = {International Journal of Metadata, Semantics and Ontologies}, volume = {13}, year = {2018}, chapter = {42}, author = {Michelle Cheatham}, editor = {Catia Pesquita and Daniela Oliveira and Helena B. McCurdy} } @article {743, title = {On the Prospects of Blockchain and Distributed Ledger Technologies for Open Science and Academic Publishing}, journal = {Semantic Web}, volume = {9}, year = {2018}, url = {http://www.semantic-web-journal.net/content/prospects-blockchain-and-distributed-ledger-technologies-open-science-and-academic}, author = {Krzysztof Janowicz and Blake Regalia and Pascal Hitzler and Gengchen Mai and Stephanie Delbecque and Maarten Fr{\"o}hlich and Patrick Mertinent and Trevor Lazarus} } @conference {708, title = {A Prot{\'e}g{\'e} Plug-In for Annotating OWL Ontologies with OPLa}, booktitle = {ESWC (Satellite Events)}, volume = {11155}, year = {2018}, pages = {23{\textendash}27}, publisher = {Springer}, organization = {Springer}, author = {Cogan Shimizu and Quinn Hirt and Pascal Hitzler} } @conference {748, title = {Pseudo-Random ALC Syntax Generation}, booktitle = {The Semantic Web: ESWC 2018 Satellite Events - ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers}, volume = {11155}, year = {2018}, pages = {19{\textendash}22}, publisher = {Springer}, organization = {Springer}, address = {Heraklion, Crete, Greece}, abstract = {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.}, keywords = {ALC, Description Logic, DL, random generation, synthetic data}, isbn = {978-3-319-98191-8}, doi = {10.1007/978-3-319-98192-5\_4}, url = {https://doi.org/10.1007/978-3-319-98192-5\_4}, author = {Aaron Eberhart and Michelle Cheatham and Pascal Hitzler} } @article {717, title = {Reasoning over RDF Knowledge Bases using Deep Learning}, journal = {arXiv preprint arXiv:1811.04132}, year = {2018}, abstract = {

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.

}, url = {https://arxiv.org/abs/1811.04132}, author = {Ebrahimi, Monireh and Md Kamruzzaman Sarker and Bianchi, Federico and Xie, Ning and Doran, Derek and Pascal Hitzler} } @conference {744, title = {Some Open Issues After Twenty Years of Formal Ontology}, booktitle = {FOIS 2018}, year = {2018}, url = {http://ebooks.iospress.nl/publication/50236}, author = {Stefano Borgo and Pascal Hitzler} } @conference {705, title = {Towards a Comprehensive Modular Ontology {IDE} and Tool Suite}, booktitle = {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.}, volume = {2181}, year = {2018}, pages = {65{\textendash}72}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, url = {http://ceur-ws.org/Vol-2181/paper-08.pdf}, author = {Cogan Shimizu} } @conference {706, title = {Towards a Pattern-Based Ontology for Chemical Laboratory Procedures}, booktitle = {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.}, volume = {2195}, year = {2018}, pages = {40{\textendash}51}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, url = {http://ceur-ws.org/Vol-2195/research_paper_1.pdf}, author = {Cogan Shimizu and Leah McEwen and Quinn Hirt} } @unpublished {746, title = {A Tutorial on Modular Ontology Modeling with Ontology Design Patterns: The Cooking Recipes Ontology}, year = {2018}, author = {Pascal Hitzler and Adila A. Krisnadhi} } @book {794, title = {Advances in Ontology Design and Patterns}, series = {Studies on the Semantic Web}, volume = {32}, year = {2017}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, author = {Karl Hammar and Pascal Hitzler and Agnieszka Lawrynowicz and Adila Krisnadhi and Andrea Nuzzolese and Monika Solanki} } @article {636, title = {On the Challenges of Sentiment Analysis for Dynamic Events}, journal = {IEEE Intelligent Systems}, year = {2017}, author = {Ebrahimi, Monireh and Yazdavar, Amir Hossein and Sheth, Amit} } @article {779, title = {Challenges of Sentiment Analysis for Dynamic Events}, year = {2017}, abstract = {

Efforts to assess people{\textquoteright}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{\textquoteright}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{\textquoteright}s Twitris system.

}, author = {Monireh Ebrahimi and Amir Hossein Yazdavar and Amit Sheth} } @conference {652, title = {Computational Environment: An ODP to Support Finding and Recreating Computational Analyses}, booktitle = {8th Workshop on Ontology Design and Patterns - WOP2017}, year = {2017}, author = {Michelle Cheatham and Charles Vardeman and Nazifa Karima and Pascal Hitzler} } @inbook {650, title = {A Core Pattern for Events}, booktitle = {Advances in Ontology Design and Patterns}, year = {2017}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, author = {Adila A. Krisnadhi and Pascal Hitzler} } @mastersthesis {633, title = {Efficient Reasoning Algorithms for Fragments of Horn Description Logics}, volume = {Doctor of Philosophy (PhD)}, year = {2017}, pages = {70}, school = {Wright State University}, address = {Dayton}, abstract = {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. }, keywords = {Description Logic, Knowledge representation, Reasoning}, url = {http://rave.ohiolink.edu/etdc/view?acc_num=wright1491317096530938}, author = {David Carral} } @proceedings {606, title = {Explaining Trained Neural Networks with Semantic Web Technologies: First Steps}, journal = {Twelveth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy}, year = {2017}, month = {07/2017}, edition = {12}, address = {London, UK}, abstract = {

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.

}, keywords = {Artificial Intelligence}, url = {http://daselab.cs.wright.edu/nesy/NeSy17/}, author = {Md Kamruzzaman Sarker and Ning Xie and Derek Doran and Michael Raymer and Pascal Hitzler} } @conference {653, title = {On the Ontological Modeling of Trees}, booktitle = {8th Workshop on Ontology Design and Patterns - WOP2017}, year = {2017}, author = {David Carral and Pascal Hitzler and Hilmar Lapp and Sebastian Rudolph} } @article {394, title = {An Ontology Design Pattern and Its Use Case for Modeling Material Transformation}, journal = {Semantic Web}, volume = {8}, year = {2017}, pages = {731}, chapter = {719}, doi = {10.3233/SW-160231}, author = {Charles Vardeman and Adila Krisnadhi and Michelle Cheatham and Krzysztof Janowicz and Holly Ferguson and Pascal Hitzler and Aimee Buccellato} } @conference {614, title = {An Ontology Design Pattern for Microblog Entries}, booktitle = {8th Workshop on Ontology Design and Patterns (WOP2017)}, year = {2017}, abstract = {

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.

}, author = {Cogan Shimizu and Michelle Cheatham} } @conference {608, title = { Propositional rule extraction from neural networks under background knowledge}, booktitle = {Twelfth International Workshop on Neural-Symbolic Learning and Reasoning}, year = {2017}, month = {07/2017}, keywords = {Background knowledge, Neural Network, Propositional Logic, Rule Extraction}, author = {Maryam Labaf}, editor = {Pascal Hitzler} } @mastersthesis {607, title = {Propositional Rule Extraction from Neural Networks under Background Knowledge}, volume = {Master}, year = {2017}, month = {07/2017}, pages = {50}, type = {Master thesis}, abstract = {

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.

}, author = {Maryam Labaf and Pascal Hitzler and Anthony B. Evans} } @conference {778, title = {Relatedness-based Multi-Entity Summarization}, booktitle = {IJCAI}, year = {2017}, abstract = {

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{\textquoteright}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.

}, author = {Kalpa Gunaratna and Amir Hossein Yazdavar and Krishnaprasad Thirunarayan and Amit Sheth and Gong Cheng} } @proceedings {647, title = {Relating Input Concepts to Convolutional Neural Network Decisions}, journal = {NIPS 2017 Workshop: Interpreting, Explaining and Visualizing Deep Learning, NIPS IEVDL 2017}, year = {2017}, month = {12/2017}, publisher = {NIPS}, address = {CA, USA}, abstract = {

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{\textquoteright}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{\textquoteright}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.

}, author = {Ning Xie and Md Kamruzzaman Sarker and Derek Doran and Pascal Hitzler and Michael Raymer} } @conference {655, title = {Rendering OWL in Description Logic Syntax}, booktitle = {ESWC 2017}, year = {2017}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg}, author = {Cogan Shimizu and Pascal Hitzler and Matthew Horridge} } @mastersthesis {615, title = {Rendering OWL in LaTeX for Improved Readability: Extensions to the OWLAPI}, volume = {Master of Science}, year = {2017}, month = {08/2017}, pages = {106}, school = {Wright State University}, address = {Dayton, Ohio}, abstract = {

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{\textquoteright}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.

}, author = {Cogan Shimizu} } @article {609, title = {A Replication Study: Understanding What Drives the Performance in WikiMatch}, year = {2017}, abstract = {

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{\textquoteright}s manually curated synonym sets, as encoded in the site{\textquoteright}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.

}, url = {http://disi.unitn.it/~pavel/om2017/papers/om2017_poster5.pdf}, author = {Lu Zhou and Michelle Cheatham} } @booklet {586, title = {Rule-based OWL Modeling with ROWLTab Protege Plugin}, year = {2017}, abstract = {

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.

}, author = {Md Kamruzzaman Sarker and Adila Krisnadhi and David Carral and Pascal Hitzler} } @conference {637, title = {Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media}, booktitle = {ASONAM}, year = {2017}, author = {Yazdavar, Amir Hossein and Al-Olimat, Hussein S and Ebrahimi, Monireh and Bajaj, Goonmeet and Banerjee, Tanvi and Thirunarayan, Krishnaprasad and Pathak, Jyotishman and Sheth, Amit} } @conference {776, title = {Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media}, booktitle = {ASONAM}, year = {2017}, abstract = {

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\%.

}, url = {https://dl.acm.org/doi/abs/10.1145/3110025.3123028}, author = {Amir Hossein Yazdavar}, editor = {Hussein S. Al-Olimat and Monireh Ebrahimi and Goonmeet Bajaj and Tanvi Banerjee and Krishnaprasad Thirunarayan and Jyotishman Pathak and Amit Sheth} } @inbook {651, title = {A Spatiotemporal Extent Pattern based on Semantic Trajectories}, booktitle = {Advances in Ontology Design and Patterns}, year = {2017}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, author = {Adila A. Krisnadhi and Pascal Hitzler and Krzysztof Janowicz} } @inbook {649, title = {The Stub Metapattern}, booktitle = {Advances in Ontology Design and Patterns}, year = {2017}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, author = {Adila A. Krisnadhi and Pascal Hitzler} } @conference {654, title = {Towards a simple but useful ontology design pattern representation language}, booktitle = {8th Workshop on Ontology Design and Patterns - WOP2017}, year = {2017}, author = {Pascal Hitzler and Aldo Gangemi and Krzysztof Janowicz and Adila A. Krisnadhi and Valentina Presutti} } @article {593, title = {AI for Traffic Analytics}, journal = {The IEEE Intelligent Informatics Bulletin}, volume = {17}, year = {2016}, chapter = {21}, author = {Raghava Mutharaju and Freddy L{\'e}cu{\'e} and Jeff Z. Pan and Jiewen Wu and Pascal Hitzler} } @inbook {569, title = {Collected Research Questions Concerning Ontology Design Patterns}, booktitle = {Ontology Engineering with Ontology Design Patterns: Foundations and Applications}, year = {2016}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, author = {Karl Hammar and Eva Blomqvist and David Carral and Marieke van Erp and Antske Fokkens and Aldo Gangemi and Willem Robert van Hage and Pascal Hitzler and Krzysztof Janowicz and Nazifa Karima and Adila Krisnadhi and Tom Narock and Roxane Segers and Monika Solanki and Vojtech Svatek} } @article {480, title = {Considerations regarding Ontology Design Patterns}, journal = {Semantic Web}, volume = {7}, year = {2016}, pages = {1-7}, author = {Eva Blomqvist and Pascal Hitzler and Krzysztof Janowicz and Adila Krisnadhi and Thomas Narock and Monika Solanki} } @conference {479, title = {The Detector Final State pattern: Using the Web Ontology Language to describe a Physics Analysis}, booktitle = {Presented at the 17th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Valparaiso, Chile, January 2016.}, year = {2016}, note = {

Presented at the\ 17th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Valparaiso, Chile, January 2016.

}, abstract = {

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{\textquoteright}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.

}, author = {Gordon Watts and Charles Vardeman and David Carral and Pascal Hitzler} } @mastersthesis {589, title = {Efficient Reasoning Algorithms for Fragments of Horn Description Logics}, volume = {PhD}, year = {2016}, school = {Wright State University}, type = {Dissertation}, address = {Dayton, OH, USA}, abstract = {

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.

}, author = {David Carral} } @article {525, title = {Final State Detector ODP OWL Ontology}, year = {2016} } @article {524, title = {Final State Detector ODP RDF Data}, year = {2016} } @article {639, title = {Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences}, journal = {Journal of Soft Computing and Decision Support Systems}, volume = {3}, year = {2016}, pages = {7{\textendash}18}, author = {Yazdavar, Amir Hossein and Ebrahimi, Monireh and Salim, Naomie} } @inbook {538, title = {Geospatial Semantic Web}, booktitle = {The International Encyclopedia of Geography: People, the Earth, Environment, and Technology. Wiley/AAG}, year = {2016}, author = {Krzysztof Janowicz and Pascal Hitzler} } @inbook {416, title = {Geospatial Semantic Web}, booktitle = {The International Encyclopedia of Geography: People, the Earth, Environment, and Technology}, year = {2016}, publisher = {Wiley/AAG}, organization = {Wiley/AAG}, author = {Krzysztof Janowicz and Pascal Hitzler} } @proceedings {594, title = {How to Document Ontology Design Patterns}, journal = {7th Workshop on Ontology and Semantic Web Patterns (WOP2016)}, year = {2016}, publisher = {IOS Press}, address = {Kobe, Japan}, author = {Nazifa Karima and Karl Hammar and Pascal Hitzler} } @inbook {567, title = {Introduction: Ontology Design Patterns in a Nutshell}, booktitle = {Ontology Engineering with Ontology Design Patterns: Foundations and Applications}, year = {2016}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, author = {Krzysztof Janowicz and Aldo Gangemi and Pascal Hitzler and Adila Krisnadhi and Valentina Presutti} } @article {537, title = {Linked Dataset Description Papers at the Semantic Web Journal: A Critical Assessment}, journal = {Semantic Web}, volume = {7}, year = {2016}, chapter = {105}, author = {Aidan Hogan and Pascal Hitzler and Krzysztof Janowicz} } @inbook {612, title = {Linked Ocean Data 2.0}, booktitle = {Oceanographic and Marine Cross-Domain Data Management for Sustainable Development}, year = {2016}, pages = {69-99}, author = {Adam Leadbetter and Michelle Cheatham and Adam Shepherd and Rob Thomas} } @conference {684, title = {LinkGen: Multipurpose linked data generator}, booktitle = {International Semantic Web Conference}, year = {2016}, pages = {113{\textendash}121}, publisher = {Springer}, organization = {Springer}, author = {Joshi, Amit Krishna and Pascal Hitzler and Dong, Guozhu} } @conference {574, title = {Matching Instances in GeoLink}, booktitle = {Ontology Matching Workshop -- ISWC}, year = {2016}, author = {Michelle Cheatham and Reihaneh Amini and Chandan Patel} } @conference {572, title = {Modeling OWL with Rules: The ROWL Protege Plugin}, year = {2016}, publisher = {15th International Semantic Web Conference (ISWC) 2016}, organization = {15th International Semantic Web Conference (ISWC) 2016}, address = {Kobe, Japan}, abstract = {

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{\textasciiacute} 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.

}, url = {http://ceur-ws.org/Vol-1690/paper92.pdf}, author = {Md Kamruzzaman Sarker and David Carral and Adila Krisnadhi and Pascal Hitzler} } @inbook {577, title = {Modeling With Ontology Design Patterns: Chess Games As a Worked Example}, booktitle = {Ontology Engineering with Ontology Design Patterns: Foundations and Applications}, volume = {25}, number = {Studies on the Semantic Web}, year = {2016}, pages = {3{\textendash}21}, publisher = {IOS Press}, organization = {IOS Press}, chapter = {1}, doi = {10.3233/978-1-61499-676-7-3}, author = {Adila Krisnadhi and Pascal Hitzler} } @conference {610, title = {A Modification to the Hazardous Situation ODP to Support Risk Assessment and Mitigation}, booktitle = {Workshop on Ontology Design Patterns (WOP)}, year = {2016}, month = {10/2016}, abstract = {

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.

}, keywords = {hazard, Ontology Design Pattern, risk assessment, risk mitigation}, author = {Michelle Cheatham and Ferguson, Holly and Charles, Vardeman and Cogan Shimizu} } @article {553, title = {Modified Hazardous Situation ODP}, year = {2016}, abstract = {

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.

}, author = {Michelle Cheatham and Ferguson, Holly and Vardeman, Charles and Cogan Shimizu} } @article {501, title = {Modular Ontology Architecture for Data Integration in the GeoLink Project}, year = {2016}, address = {Ontology Summit 2016 (online)}, url = {http://ontologforum.org/index.php?title=ConferenceCall_2016_02_25\&oldid=22543$\#$hid1C2C}, author = {Adila Krisnadhi} } @inbook {579, title = {Ontology Design Patterns for Data Integration: The {G}eo{L}ink Experience}, booktitle = {Ontology Engineering with Ontology Design Patterns: Foundations and Applications}, volume = {25}, number = {Studies on the Semantic Web}, year = {2016}, pages = {267 - 278}, chapter = {13}, doi = {10.3233/978-1-61499-676-7-267}, author = {Adila Krisnadhi} } @inbook {578, title = {Ontology Design Patterns for Linked Data Publishing}, booktitle = {Ontology Engineering with Ontology Design Patterns: Foundations and Applications}, volume = {25}, number = {Studies on the Semantic Web}, year = {2016}, pages = {201 - 232}, chapter = {10}, doi = {10.3233/978-1-61499-676-7-201}, author = {Adila Krisnadhi and Nazifa Karima and Pascal Hitzler and Reihaneh Amini and V{\'\i}ctor Rodr{\'\i}guez-Doncel and Krzysztof Janowicz} } @book {566, title = {Ontology Engineering with Ontology Design Patterns: Foundations and Applications}, series = {Studies On the Semantic Web}, volume = {025}, year = {2016}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, author = {Pascal Hitzler and Aldo Gangemi and Krzysztof Janowicz and Adila Krisnadhi and Valentina Presutti} } @conference {573, title = {OWLAx: A Protege Plugin to Support Ontology Axiomatization through Diagramming}, year = {2016}, publisher = {15th International Semantic Web Conference, ISWC2016, Kobe, Japan, October 2016}, organization = {15th International Semantic Web Conference, ISWC2016, Kobe, Japan, October 2016}, address = {Kobe, Japan}, abstract = {

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{\textasciiacute}eg{\textasciiacute}e3 plugin which supports this task, together with a visual user interface, based on established methods for ontology design pattern modeling.

}, url = {http://ceur-ws.org/Vol-1690/paper83.pdf}, author = {Md Kamruzzaman Sarker and Adila Krisnadhi and Pascal Hitzler} } @conference {556, title = {A Practical Acyclicity Notion for Query Answering Over Horn-SRIQ Ontologies}, booktitle = {The Semantic Web - {ISWC} 2016 - 15th International Semantic Web Conference, Kobe, Japan, October 17-21, 2016, Proceedings, Part {I}}, year = {2016}, pages = {70{\textendash}85}, abstract = {

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.

}, doi = {10.1007/978-3-319-46523-4_5}, url = {http://dx.doi.org/10.1007/978-3-319-46523-4_5}, author = {David Carral and Cristina Feier and Pascal Hitzler} } @inbook {580, title = {The {R}ole Patterns}, booktitle = {Ontology Engineering with Ontology Design Patterns: Foundations and Applications}, volume = {25}, number = {Studies on the Semantic Web}, year = {2016}, pages = {313{\textendash}319}, publisher = {IOS Press}, organization = {IOS Press}, chapter = {16}, doi = {10.3233/978-1-61499-676-7-313}, author = {Adila Krisnadhi} } @conference {570, title = {Reasoning with Large Scale OWL 2 EL Ontologies Based on MapReduce}, booktitle = {Web Technologies and Applications - 18th Asia-Pacific Web Conference, APWeb 2016, Suzhou, China, September 23-25, 2016}, year = {2016}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg}, author = {Zhangquan Zhou and Guilin Qi and Chang Liu and Raghava Mutharaju and Pascal Hitzler} } @article {638, title = {Recognition of side effects as implicit-opinion words in drug reviews}, journal = {Online Information Review}, volume = {40}, year = {2016}, pages = {1018{\textendash}1032}, author = {Ebrahimi, Monireh and Yazdavar, Amir Hossein and Salim, Naomie and Eltyeb, Safaa} } @article {780, title = {Recognition of side effects as implicit-opinion words in drug reviews}, year = {2016}, abstract = {

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{\textquoteright}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.

}, author = {Monireh Ebrahimi and Amir HosseinYazdavar and Naomie Salim and Safaa Eltyeb} } @article {640, title = {Rock strength estimation: a PSO-based BP approach}, journal = {Neural Computing and Applications}, year = {2016}, pages = {1{\textendash}12}, author = {Mohamad, E Tonnizam and Armaghani, D Jahed and Momeni, E and Yazdavar, AH and Ebrahimi, M} } @inbook {568, title = {On the Roles of Logical Axiomatizations for Ontologies}, booktitle = {Ontology Engineering with Ontology Design Patterns: Foundations and Applications}, year = {2016}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, author = {Pascal Hitzler and Adila Krisnadhi} } @inbook {611, title = {Semantic Data Integration}, booktitle = {Springer Handbook on Big Data}, year = {2016}, author = {Michelle Cheatham and Pesquita, Catia} } @conference {575, title = {Semantic Web Enabled Record Linkage Attacks on Anonymized Data}, booktitle = {PrivOn Workshop at ISWC}, year = {2016}, author = {Jacob Miracle and Michelle Cheatham} } @conference {543, title = {Towards Best Practices for Crowdsourcing Ontology Alignment Benchmarks}, booktitle = {15th International Semantic Web Conference (ISWC) 2016}, year = {2016}, address = {Kobe, Japan}, abstract = {

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{\textquoteright}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.

}, author = {Reihaneh Amini and Michelle Cheatham and Pawel Grzebala and Helena B. McCurdy} } @conference {508, title = {Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones}, booktitle = {International Conference on World Wide Web }, year = {2016}, month = {04/2016}, publisher = {International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland}, organization = {International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland}, abstract = {

}, isbn = {978-1-4503-4143-1}, doi = {doi>10.1145/2872427.2883052}, author = {Lu Zhou and Wenbo Wang and Keke Chen} } @article {581, title = {Update on ESIP Testbed Project}, year = {2016}, author = {Nazifa Karima and Adila Krisnadhi and Pascal Hitzler and Tom Narock} } @article {554, title = {Use case for the Modified Hazardous Situation ODP}, year = {2016}, author = {Michelle Cheatham and Cogan Shimizu and Holly Ferguson and Charles Vardeman} } @conference {496, title = {Alignment Aware Linked Data Compression}, booktitle = {Joint International Semantic Technology Conference}, year = {2015}, abstract = {

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.

}, author = {Joshi, Amit Krishna and Pascal Hitzler and Dong, Guozhu} } @conference {157, title = {Are We Really Standing on the Shoulders of Giants?}, booktitle = {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) }, year = {2015}, address = {Portoroz, Slovenia}, author = {Raghava Mutharaju and Pavan Kapanipathi} } @conference {362, title = {The Combined Approach to Query Answering Beyond the OWL 2 Profiles}, booktitle = {Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI)}, year = {2015}, abstract = {

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.

}, url = {http://www.cs.ox.ac.uk/isg/people/cristina.feier/ijcai_rsafinal.pdf}, author = {Cristina Feier and David Carral and Giorgio Stefanoni and Cuenca Grau, Bernardo and Ian Horrocks} } @article {472, title = {Data Perturbation via Randomized Normalization for Privacy Protection}, year = {2015}, author = {Reihaneh Amini and Michelle Cheatham and Nazifa Karima} } @conference {156, title = {Distributed and Scalable OWL EL Reasoning}, booktitle = {Proceedings of the 12th Extended Semantic Web Conference (ESWC 2015) }, year = {2015}, publisher = {Springer}, organization = {Springer}, address = {Portoroz, Slovenia}, abstract = {

OWL 2 EL is one of the tractable proles of the Web Ontology\ Language (OWL) which is a W3C-recommended standard. OWL 2\ EL provides sucient expressivity to model large biomedical ontologies\ as well as streaming data such as trac, while at the same time allows\ for ecient reasoning services. Existing reasoners for OWL 2 EL, however,\ use only a single machine and are thus constrained by memory and\ computational power. At the same time, the automated generation of\ ontological information from streaming data and text can lead to very\ large ontologies which can exceed the capacities of these reasoners. We\ thus describe a distributed reasoning system that scales well using a cluster\ of commodity machines. We also apply our system to a use case on\ city trac data and show that it can handle volumes which cannot be\ handled by current single machine reasoners.

}, keywords = {DistEL, Distributed Reasoning, Ontology Classification, OWL EL}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti and Freddy L{\'e}cu{\'e}} } @conference {421, title = {Distributed Reasoning over Ontology Streams and Large Knowledge Base}, booktitle = {NSF Data Science Workshop 2015}, year = {2015}, address = {Seattle, USA}, author = {Raghava Mutharaju} } @conference {481, title = {EarthCube GeoLink: Semantics and Linked Data for the Geosciences}, booktitle = {2015 American Geophysical Union Fall Meeting, San Francisco, 14-18 December 2015}, year = {2015}, author = {Robert A. Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Douglas Fils and Pascal Hitzler and Krzysztof Janowicz and Peng Ji and Matthew Jones and Adila Krisnadhi and Kerstin Lehnert and Audrey Mickle and Tom Narock and Margaret O{\textquoteright}Brien and Lisa Raymond and Mark Schildhauer and Adam Shepherd and Peter Wiebe} } @conference {363, title = {Extending the Combined Approach Beyond Lightweight Description Logics}, booktitle = {Proceedings of the 28th International Workshop on Description Logics (DL)}, year = {2015}, abstract = {

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.

}, url = {http://www.cs.ox.ac.uk/isg/people/cristina.feier/pdfs/dlmain.pdf}, author = {Cristina Feier and David Carral and Giorgio Stefanoni and Cuenca Grau, Bernardo and Ian Horrocks} } @article {392, title = {The {GeoLink} Framework for Pattern-based Linked Data Integration}, journal = {Proceedings of the ISWC 2015 Posters \& Demonstrations Track}, year = {2015}, month = {10/2015}, author = {Adila Krisnadhi and Yingjie Hu and Krzsyztof Janowicz and Pascal Hitzler and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Douglas Fils and Timothy Finin and Peng Ji and Matthew Jones and Nazifa Karima and Kerstin Lehnert and Audrey Mickle and Thomas Narock and Margaret O{\textquoteright}Brien and Lisa Raymond and Adam Shepherd and Mark Schildhauer and Peter Wiebe} } @conference {393, title = {The {GeoLink} Modular Oceanography Ontology}, booktitle = {The Semantic Web - ISWC 2015. 14th International Semantic Web Conference, Bethlehem, Pennsylvania, United States, October 11-15, 2015}, year = {2015}, month = {10/2015}, publisher = {Springer}, organization = {Springer}, author = {Adila Krisnadhi and Yingjie Hu and Krzysztof Janowicz and Pascal Hitzler and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Douglas Fils and Timothy Finin and Peng Ji and Matthew Jones and Nazifa Karima and Kerstin Lehnert and Audrey Mickle and Thomas Narock and Margaret O{\textquoteright}Brien and Lisa Raymond and Adam Shepherd and Mark Schildhauer and Peter Wiebe} } @article {497, title = {Identifying Regrettable Messages from Tweets}, year = {2015}, month = {05/2015}, publisher = {International World Wide Web Conferences Steering Committee}, type = {Posters}, isbn = {978-1-4503-3473-0}, doi = {doi>10.1145/2740908.2742735}, author = {Lu Zhou and Wenbo Wang and Keke Chen} } @mastersthesis {422, title = {A Language for Inconsistency-Tolerant Ontology Mapping}, volume = {Doctor of Philosophy}, year = {2015}, abstract = {

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.

}, author = {Kunal Sengupta} } @conference {482, title = {Linked Data: Forming Partnerships at the Data Layer}, booktitle = {2015 American Geophysical Union Fall Meeting, San Francisco, 14-18 December 2015}, year = {2015}, author = {Adam Shepherd and Cynthia Chandler and Robert A. Arko and Matthew Jones and Pascal Hitzler and Krzysztof Janowicz and Adila Krisnadhi and Mark Schildhauer and Douglas Fils and Tom Narock and Robert Groman and Margaret O{\textquoteright}Brien and Evan W. Patton and Danie Kinkade and Shannon Rauch} } @conference {388, title = {A Minimal Ontology Pattern for Life Cycle Assessment Data}, booktitle = {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}, year = {2015}, month = {10/2015}, author = {Krzysztof Janowicz and Adila Krisnadhi and Yingjie Hu and Sangwon Suh and Bo Pedersen Weidema and Beatriz Rivela and Johan Tivander and David E. Meyer and Gary Berg-Cross and Pascal Hitzler and Wesley Ingwersen and Brandon Kuczenski and Charles Vardeman and Yiting Ju} } @conference {326, title = {Neural-Symbolic Learning and Reasoning: Contributions and Challenges}, booktitle = {AAAI 2015 Spring Symposium on Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches. Technical Report SS-15-03, AAAI Press, Palo Alto}, year = {2015}, publisher = {AAAI Press}, organization = {AAAI Press}, author = {Artur S. d{\textquoteright}Avila Garcez and Tarek R. Besold and Luc De Raedt and Peter F{\"o}ldiak and Pascal Hitzler and Thomas Icard and Kai-Uwe K{\"u}hnberger and Lu{\'\i}s C. Lamb and Riisto Miikkulainen and Daniel L. Silver} } @conference {483, title = {Ontological Support of Data Discovery and Synthesis in Estuarine and Coastal Science}, booktitle = {CERF 2015: 23rd Biennial Confernence, Grand Challenges in Coastal and Estuarine Science: Securing Our Future, Portland, OR, November 2015}, year = {2015}, author = {Anne Thessen and Benjamin Fertig and Ramona Walls and Pascal Hitzler and Rick Ziegler} } @conference {389, title = {An Ontology Design Pattern for Chess Games}, booktitle = {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}, volume = {1461}, year = {2015}, month = {10/2015}, url = {http://ceur-ws.org/Vol-1461/WOP2015_pattern_abstract_2.pdf}, author = {Adila Krisnadhi and V{\'\i}ctor Rodr{\'\i}guez-Doncel and Pascal Hitzler and Michelle Cheatham and Nazifa Karima and Reihaneh Amini and Ashley Coleman} } @conference {414, title = {An Ontology Design Pattern for Data Integration in the Library Domain}, booktitle = {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}, year = {2015}, month = {10/2015}, abstract = {

A university{\textquoteright}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.

}, author = {Patrick Obrien and David Carral and Jeff Mixter and Pascal Hitzler} } @conference {387, title = {An Ontology Design Pattern for Dynamic Relative Relationships}, booktitle = {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}, volume = {1461}, year = {2015}, month = {10/2015}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, url = {http://ceur-ws.org/Vol-1461/WOP2015_paper_3.pdf}, author = {Holly Ferguson and Adila Krisnadhi and Charles Vardeman}, editor = {Eva Blomqvist and Pascal Hitzler and Adila Krisnadhi and Thomas Narock and Monika Solanki} } @conference {386, title = {An Ontology Design Pattern for Particle Physics Analysis}, booktitle = {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}, volume = {1461}, year = {2015}, month = {10/2015}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {

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.

}, url = {http://ceur-ws.org/Vol-1461/WOP2015_pattern_abstract_5.pdf}, author = {David Carral and Michelle Cheatham and Sunje Dallmeir-Tiessen and Patricia Herterich and Michael D. Hildreth and Pascal Hitzler and Adila Krisnadhi and Kati Lassila-Perini and Elizabeth Sexton-Kennedy and Charles Vardeman and Gordon Watts}, editor = {Eva Blomqvist and Pascal Hitzler and Adila Krisnadhi and Thomas Narock and Monika Solanki} } @conference {321, title = {Ontology Design Patterns: Bridging the Gap Between Local Semantic Use Cases and Large-Scale, Long-Term Data Integration}, booktitle = {European Geosciences Union General Assembly 2015, Vienna, Austria, 12 - 17 April 2015}, year = {2015}, author = {Adam Shepherd and Robert Arko and Adila Krisnadhi and Pascal Hitzler and Krzysztof Janowicz and Cynthia Chandler and Thomas Narock and Michelle Cheatham and Mark Schildhauer and Matthew Jones and Lisa Raymond and Audrey Mickle and Timothy Finin and Douglas Fils} } @conference {484, title = {Ontology Design Patterns for Semantically Enriched LCA}, booktitle = {LCA XV, University of British Columbia, Vancouver, Canada, October 6-8, 2015}, year = {2015}, author = {Brandon Kuczenski and Wesley Ingwersen and Krzysztof Janowicz and Pascal Hitzler and Gary Berg-Cross and Charles Vardeman and Sangwon Suh} } @conference {390, title = {An Ontology For Specifying Spatiotemporal Scopes in Life Cycle Assessment}, booktitle = {Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015}, volume = {1501}, year = {2015}, month = {10/2015}, pages = {25-30}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, url = {http://ceur-ws.org/Vol-1501/Diversity2015-paper_4.pdf}, author = {Bo Yan and Yingjie Hu and Brandon Kuczenski and Krzsyztof Janowicz and Andrea Ballatore and Adila Krisnadhi and Pascal Hitzler and Sangwon Suh and Wesley Ingwersen}, editor = {Claudia d{\textquoteright}Amato and Freddy L{\'e}cu{\'e} and Raghava Mutharaju and Thomas Narock and Fabian Wirth} } @conference {485, title = {Ontology modeling with domain experts}, booktitle = {1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015}, year = {2015}, author = {Pascal Hitzler} } @conference {486, title = {Ontology modeling with domain experts: The GeoVoCamp experience}, booktitle = {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}, year = {2015}, author = {Pascal Hitzler and Krzysztof Janowicz and Adila Krisnadhi} } @inbook {385, title = {Ontology Pattern Modeling for Cross-Repository Data Integration in the Ocean Sciences: The Oceanographic Cruise Example}, booktitle = {The Semantic Web in Earth and Space Science: Current Status and Future Directions}, year = {2015}, pages = {256-284}, publisher = {IOS Press}, organization = {IOS Press}, abstract = {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.}, author = {Adila Krisnadhi and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Timothy Finin and Pascal Hitzler and Krzysztof Janowicz and Thomas Narock and Lisa Raymond and Adam Shepherd and Peter Wiebe} } @mastersthesis {598, title = {Ontology Pattern-Based Data Integration}, volume = {Doctor of Philosophy}, year = {2015}, month = {12/2015}, pages = {233}, school = {Wright State University}, type = {Dissertation}, address = {Dayton}, abstract = {

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.

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.

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.\ 

}, url = { http://rave.ohiolink.edu/etdc/view?acc_num=wright1453177798}, author = {Adila Krisnadhi} } @conference {383, title = {Pattern-Based Linked Data Publication: The Linked Chess Dataset Case}, booktitle = {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}, volume = {1426}, year = {2015}, month = {10/2015}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, type = {Technical Report}, abstract = {

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.

}, url = {http://dase.cs.wright.edu/publications/pattern-based-linked-data-publication-linked-chess-dataset-case}, author = {V{\'\i}ctor Rodr{\'\i}guez-Doncel and Adila Krisnadhi and Pascal Hitzler and Michelle Cheatham and Nazifa Karima and Reihaneh Amini}, editor = {Olaf Hartig and Juan Sequeda and Aidan Hogan} } @proceedings {489, title = {Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015}, journal = {CEUR Workshop Proceedings}, volume = {1501}, year = {2015}, publisher = {CEUR-WS.org}, address = {Bethlehem, PA, USA}, url = {http://ceur-ws.org/Vol-1501}, editor = {Claudia d{\textquoteright}Amato and Freddy L{\'e}cu{\'e} and Raghava Mutharaju and Tom Narock and Fabian Wirth} } @proceedings {487, title = {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}, year = {2015}, author = {Eva Blomqvist and Pascal Hitzler and Adila Krisnadhi and Tom Narock and Monika Solanki} } @conference {391, title = {{R2R+BCO-DMO} {\textendash} Linked Oceanographic Datasets}, booktitle = {Proceedings of the 1st International Diversity++ Workshop co-located with the 14th International Semantic Web Conference (ISWC 2015), Bethlehem, Pennsylvania, USA, October 12, 2015}, volume = {1501}, year = {2015}, month = {10/2015}, pages = {15-24}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {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 \& Ecosystems Programs. These two repositories have a strong connection, and document several thousand U.S. oceanographic research expeditions since the 1970{\textquoteright}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.}, author = {Adila Krisnadhi and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Pascal Hitzler and Yingjie Hu and Krzysztof Janowicz and Peng Ji and Nazifa Karima and Adam Shepherd and Peter Wiebe}, editor = {Claudia d{\textquoteright}Amato and Freddy L{\'e}cu{\'e} and Raghava Mutharaju and Thomas Narock and Fabian Wirth} } @conference {510, title = {Scalable Euclidean Embedding for Big Data}, booktitle = {International Conference on Cloud Computing }, year = {2015}, month = {07/2015}, publisher = {IEEE }, organization = {IEEE }, address = {New York City, NY }, doi = {10.1109/CLOUD.2015.107}, author = {Zohreh Alavi and Sagar Sharma and Lu Zhou and Keke Chen} } @conference {415, title = {The Semantic Web Journal as Linked Data}, booktitle = {International Semantic Web Conference, ISWC 2015, Posters and Demonstrations Track}, year = {2015}, month = {2015}, publisher = {Springer}, organization = {Springer}, author = {Yingjie Hu and Krzysztof Janowicz and Pascal Hitzler and Kunal Sengupta} } @article {417, title = {The Semantic Web Journal Review Process: Transparent and Open}, journal = {IEEE Computer Society Special Technical Community on Social Networking E-Letter}, volume = {3}, year = {2015}, month = {06/2015}, author = {Pascal Hitzler and Krzysztof Janowicz} } @article {54, title = {Semantics for Big Data}, journal = {AI Magazine}, volume = {36}, year = {2015}, pages = {3{\textendash}4}, url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2559}, author = {Frank van Harmelen and James A. Hendler and Pascal Hitzler and Krzysztof Janowicz} } @conference {420, title = {Social Signal Processing for Real-time Situational Understanding: a Vision and Approach}, booktitle = {First International Workshop on Social Sensing (SocialSens 2015)}, year = {2015}, address = {Dallas, Texas, USA}, abstract = {

The US Army Research Laboratory (ARL) and\ the Air Force Research Laboratory (AFRL) have established a\ collaborative research enterprise referred to as the Situational\ Understanding Research Institute (SURI). The goal is to develop\ an information processing framework to help the military obtain\ real-time situational awareness of physical events by harnessing\ the combined power of multiple sensing sources to obtain insights\ about events and their evolution. It is envisioned that one could\ use such information to predict behaviors of groups, be they local\ transient groups (e.g., protests) or widespread, networked groups,\ and thus enable proactive prevention of nefarious activities. This\ paper presents a vision of how social media sources can be\ exploited in the above context to obtain insights about events,\ groups, and their evolution.

}, author = {Kasthuri Jayarajah and Shuochao Yao and Raghava Mutharaju and Archan Misra and Geeth De Mel and Julie Skipper and Tarek Abdelzaher and Michael Kolodny} } @conference {488, title = {Towards a Rule Based Distributed OWL Reasoning Framework}, booktitle = {12th OWL Experiences and Directions Workshop (OWLED 2015) co-located with the 14th International Semantic Web Conference (ISWC 2015)}, year = {2015}, publisher = {Springer}, organization = {Springer}, address = {Bethlehem, PA, USA}, abstract = {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.}, author = {Raghava Mutharaju and Prabhaker Mateti and Pascal Hitzler} } @conference {381, title = {Towards Defeasible Mappings for Tractable Description Logics}, booktitle = {ISWC 2015 - 14th International Semantic Web Conference, Bethlehem, PA, USA, October 11-15, 2015}, year = {2015}, pages = {237-252}, publisher = {Springer}, organization = {Springer}, author = {Kunal Sengupta and Pascal Hitzler} } @article {55, title = {Why the Data Train Needs Semantic Rails}, journal = {AI Magazine}, volume = {36}, year = {2015}, pages = {5{\textendash}14}, abstract = {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{\textquoteright}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.}, url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2560}, author = {Krzysztof Janowicz and Frank van Harmelen and James A. Hendler and Pascal Hitzler} } @conference {133, title = {All But Not Nothing: Left-Hand Side Universals for Tractable {OWL} Profiles}, booktitle = {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.}, volume = {1265}, year = {2014}, month = {10/2014}, pages = {97-108}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {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.}, keywords = {description logics, Horn Logics, OWL}, url = {http://ceur-ws.org/Vol-1265/owled2014_submission_13.pdf}, author = {David Carral and Adila Krisnadhi and Sebastian Rudolph and Pascal Hitzler}, editor = {C. Maria Keet and Valentina A. M. Tamma} } @article {642, title = {Analytical modeling and simulation of I{\textendash}V characteristics in carbon nanotube based gas sensors using ANN and SVR methods}, journal = {Chemometrics and Intelligent Laboratory Systems}, volume = {137}, year = {2014}, pages = {173{\textendash}180}, author = {Akbari, Elnaz and Buntat, Zolkafle and Enzevaee, Aria and Ebrahimi, Monireh and Yazdavar, Amir Hossein and Yusof, Rubiyah} } @proceedings {147, title = {Artificial Intelligence: Methodology, Systems, and Applications - 16th International Conference, AIMSA 2014, Varna, Bulgaria, September 11-13, 2014. Proceedings}, journal = {AIMSA 2014}, volume = {8722}, year = {2014}, publisher = {Springer}, isbn = {978-3-319-10553-6}, doi = {10.1007/978-3-319-10554-3}, url = {http://dx.doi.org/10.1007/978-3-319-10554-3}, editor = {Gennady Agre and Pascal Hitzler and Adila Krisnadhi and Sergei O. Kuznetsov} } @conference {398, title = {Combining Learning and Reasoning for Big Data}, booktitle = {Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)}, volume = {9}, year = {2014}, author = {Pascal Hitzler}, editor = {Artur S. d{\textquoteright}Avila Garcez and Marco Gori and Pascal Hitzler and Lu{\'\i}s C. Lamb} } @conference {8, title = {Conference v2.0: An uncertain version of the OAEI Conference benchmark}, booktitle = {13th International Semantic Web Conference (ISWC 2014)}, volume = {8797}, year = {2014}, month = {10/2014}, pages = {148-163}, publisher = {Lecture Notes in Computer Science, Springer}, organization = {Lecture Notes in Computer Science, Springer}, address = {Riva del Garda, Italy}, abstract = {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{\textquoteright}s Mechanical Turk is introduced and shown to be scalable, cost-effective and to agree well with expert opinion.}, keywords = {benchmark, OAEI, Ontology Alignment}, author = {Michelle Cheatham and Pascal Hitzler}, editor = {Peter Mika and Tania Tudorache and Abraham Bernstein and Chris Welty and Craig A. Knoblock and Denny Vrandecic and Paul T. Groth and Natasha F. Noy and Krzysztof Janowicz and Carole A. Goble} } @inbook {90, title = {Description Logics}, booktitle = {Handbook of the History of Logic}, volume = {9}, year = {2014}, pages = {679-710}, publisher = {Elsevier}, organization = {Elsevier}, author = {Matthias Knorr and Pascal Hitzler}, editor = {Dov. M. Gabbay and John Woods and J{\"o}rg Siekmann} } @inbook {148, title = {Description Logics}, booktitle = {Encyclopedia of Social Network Analysis and Mining}, year = {2014}, pages = {346-351}, publisher = {Springer}, organization = {Springer}, doi = {10.1007/978-1-4614-6170-8_108}, url = {http://dx.doi.org/10.1007/978-1-4614-6170-8_108}, author = {Adila Krisnadhi and Pascal Hitzler} } @conference {158, title = {Developing a Distributed Reasoner for the Semantic Web}, booktitle = {Proceedings of the ISWC Developers Workshop 2014, co-located with the 13th International Semantic Web Conference (ISWC 2014)}, volume = {1268}, year = {2014}, pages = {108{\textendash}112}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Riva del Garda, Italy}, abstract = {

OWL 2 EL is one of the tractable profiles of the Web Ontology Language (OWL) which has been standardized by the W3C. OWL 2\ EL provides suficient expressivity to model large biomedical ontologies\ as well streaming traffic data. Automated generation of ontologies from\ streaming data and text can lead to very large ontologies. There is a\ need to develop scalable reasoning approaches which scale with the size\ of the ontologies. We briefly describe our distributed reasoner, DistEL\ along with our experience and lessons learned during its development.

}, url = {http://ceur-ws.org/Vol-1268/paper18.pdf}, author = {Raghava Mutharaju and Prabhaker Mateti and Pascal Hitzler}, editor = {Ruben Verborgh and Erik Mannens} } @conference {11, title = {Distributed OWL EL Reasoning: The Story So Far}, booktitle = {Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy}, volume = {1261}, year = {2014}, month = {10/2014}, pages = {61-76}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Riva del Garda, Italy}, abstract = {

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).

}, keywords = {Distributed Reasoning, OWL EL, Scalability}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti}, editor = {Thorsten Liebig and Achille Fokoue} } @conference {135, title = {EL-ifying Ontologies}, booktitle = {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}, year = {2014}, pages = {464{\textendash}479}, abstract = {

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.

}, keywords = {description logics, OWL, Rewriting, Tractable Reasoning}, doi = {10.1007/978-3-319-08587-6_36}, url = {http://dx.doi.org/10.1007/978-3-319-08587-6_36}, author = {David Carral and Cristina Feier and Cuenca Grau, Bernardo and Pascal Hitzler and Ian Horrocks} } @conference {514, title = {Emotion recognition from speech based on relevant feature and majority voting}, booktitle = {ICIEV}, year = {2014}, abstract = {

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.

}, author = {Md Kamruzzaman Sarker and Kazi Md Rokibul, Alam and Md Arifuzzaman} } @conference {288, title = {Enhancing Ocean Research Data Access}, booktitle = {European Geosciences Union General Assembly 2014}, year = {2014}, address = {Vienna, Austria}, author = {Cynthia Chandler and Robert Groman and Adam Shepherd and Molly Allison and Robert Arko and Yu Chen and Peter Fox and David Glover and Pascal Hitzler and Adam Leadbetter and Thomas Narock and Patrick West and Peter Wiebe} } @article {56, title = {Five stars of Linked Data vocabulary use}, journal = {Semantic Web}, volume = {5}, year = {2014}, pages = {173{\textendash}176}, abstract = {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 {\textendash} 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{\textquoteright}s original proposal to the next level by introducing a 5 star rating for Linked Data vocabulary use.}, doi = {10.3233/SW-140135}, url = {http://dx.doi.org/10.3233/SW-140135}, author = {Krzysztof Janowicz and Pascal Hitzler and Benjamin Adams and Dave Kolas and Charles Vardeman} } @conference {152, title = {How to Best Find a Partner? An Evaluation of Editing Approaches to Construct R2RML Mappings}, booktitle = {The Semantic Web: Trends and Challenges - 11th International Conference, ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014. Proceedings}, volume = {8465}, year = {2014}, pages = {675{\textendash}690}, publisher = {Springer}, organization = {Springer}, author = {Christoph Pinkel and Carsten Binnig and Peter Haase and Clemens Martin and Kunal Sengupta and Johannes Trame}, editor = {Valentina Presutti and Claudia d{\textquoteright}Amato and Fabien Gandon and Mathieu d{\textquoteright}Aquin and Steffen Staab and Anna Tordai} } @inbook {89, title = {Logics for the Semantic Web}, booktitle = {Handbook of the History of Logic}, volume = {9}, year = {2014}, pages = {679-710}, publisher = {Elsevier}, organization = {Elsevier}, author = {Pascal Hitzler and Jens Lehmann and Axel Polleres}, editor = {Dov. M. Gabbay and John Woods and J{\"o}rg Siekmann} } @article {59, title = {Neural-Symbolic Learning and Reasoning (Dagstuhl Seminar 14381)}, journal = {Dagstuhl Reports}, volume = {4}, year = {2014}, pages = {50{\textendash}84}, doi = {10.4230/DagRep.4.9.50}, url = {http://dx.doi.org/10.4230/DagRep.4.9.50}, author = {Artur S. d{\textquoteright}Avila Garcez and Marco Gori and Pascal Hitzler and Lu{\'\i}s C. Lamb} } @conference {320, title = {The OceanLink Project}, booktitle = {American Geophysical Union Fall Meeting, San Francisco, 15-19 December 2014}, year = {2014}, author = {Thomas Narock and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Timothy Finin and Pascal Hitzler and Adila Krisnadhi and Lisa Raymond and Adam Shepherd and Peter Wiebe} } @conference {144, title = {The {OceanLink} project}, booktitle = {2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, October 27-30, 2014}, year = {2014}, month = {10/2014}, pages = {14-21}, publisher = {{IEEE}}, organization = {{IEEE}}, abstract = {Today{\textquoteright}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.}, isbn = {978-1-4799-5665-4}, doi = {10.1109/BigData.2014.7004347}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6973861}, author = {Thomas Narock and Robert Arko and Suzanne Carbotte and Adila Krisnadhi and Pascal Hitzler and Michelle Cheatham and Adam Shepherd and Cynthia Chandler and Lisa Raymond and Peter Wiebe and Timothy Finin}, editor = {Jimmy Lin and Jian Pei and Xiaohua Hu and Wo Chang and Raghunath Nambiar and Charu Aggarwal and Nick Cercone and Vasant Honavar and Jun Huan and Bamshad Mobasher and Saumyadipta Pyne} } @conference {132, title = {An Ontology Design Pattern for Activity Reasoning}, booktitle = {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.}, year = {2014}, pages = {78{\textendash}81}, abstract = {

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.

}, keywords = {Activity, Ontology Design Pattern, OWL}, url = {http://ceur-ws.org/Vol-1302/paper8.pdf}, author = {Amin Abdalla and Yingjie Hu and David Carral and Naicong Li and Krzysztof Janowicz} } @conference {145, title = {An Ontology Design Pattern for Cooking Recipes - Classroom Created}, booktitle = {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.}, volume = {1302}, year = {2014}, month = {10/2014}, pages = {49-60}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {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.}, url = {http://ceur-ws.org/Vol-1302}, author = {Monica Sam and Adila Krisnadhi and Cong Wang and John C. Gallagher and Pascal Hitzler}, editor = {de Boer, Victor and Aldo Gangemi and Krzysztof Janowicz and Agnieszka Lawrynowicz} } @conference {146, title = {An Ontology Design Pattern for Material Transformation}, booktitle = {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.}, volume = {1302}, year = {2014}, month = {10/2014}, pages = {73-77}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {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.}, url = {http://ceur-ws.org/Vol-1302}, author = {Charles Vardeman and Adila Krisnadhi and Michelle Cheatham and Krzysztof Janowicz and Holly Ferguson and Pascal Hitzler and Aimee Buccellato and Krishnaprasad Thirunarayan and Gary Berg-Cross and Torsten Hahmann}, editor = {de Boer, Victor and Aldo Gangemi and Krzysztof Janowicz and Agnieszka Lawrynowicz} } @conference {93, title = {Ontology Design Patterns for Large-Scale Data Interchange and Discovery}, booktitle = {19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014)}, volume = {8876}, year = {2014}, pages = {XIX-XX}, publisher = {Springer}, organization = {Springer}, address = {Link{\"o}ping, Sweden}, author = {Pascal Hitzler}, editor = {Krzysztof Janowicz and Stefan Schlobach and Patrick Lambrix and Eero Hyv{\"o}nen} } @conference {399, title = {Ontology Design Patterns for Ocean Science Data Discovery}, booktitle = {Spatial reference in the Semantic Web and in Robotics (Dagstuhl Seminar 14142)}, volume = {3}, year = {2014}, author = {Pascal Hitzler and Adila Krisnadhi and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Timothy Finin and Krzysztof Janowicz and Thomas Narock and Lisa Raymond and Adam Shepherd and Peter Wiebe}, editor = {Aldo Gangemi and Verena V. Hafner and Werner Kuhn and Simon Scheider and Luc Steels} } @article {306, title = {An Ontology Pattern for Oceanograhic Cruises: Towards an Oceanographer{\textquoteright}s Dream of Integrated Knowledge Discovery}, year = {2014}, abstract = {

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.

}, author = {Adila Krisnadhi and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Timothy Finin and Pascal Hitzler and Krzysztof Janowicz and Thomas Narock and Lisa Raymond and Adam Shepherd and Peter Wiebe} } @conference {102, title = {The Properties of Property Alignment}, booktitle = {Ninth International Workshop on Ontology Matching}, year = {2014}, address = {Riva del Garda, Italy}, author = {Michelle Cheatham and Pascal Hitzler} } @conference {318, title = {Provenance Usage in the OceanLink Project}, booktitle = {American Geophysical Union Fall Meeting, San Francisco, 15-19 December 2014.}, year = {2014}, author = {Thomas Narock and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Douglas Fils and Timothy Finin and Pascal Hitzler and Krzysztof Janowicz and Matthew Jones and Adila Krisnadhi and Kerstin Lehnert and Audrey Mickle and Lisa Raymond and Mark Schildhauer and Adam Shepherd and Peter Wiebe} } @conference {131, title = {Pushing the Boundaries of Tractable Ontology Reasoning}, booktitle = {The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II}, year = {2014}, pages = {148{\textendash}163}, abstract = {

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.

}, keywords = {description logics, OWL, Tractable Reasoning}, doi = {10.1007/978-3-319-11915-1_10}, url = {http://dx.doi.org/10.1007/978-3-319-11915-1_10}, author = {David Carral and Cristina Feier and Cuenca Grau, Bernardo and Pascal Hitzler and Ian Horrocks} } @article {509, title = {RASP-QS: Efficient and Confidential Query Services in the Cloud}, year = {2014}, address = {VLDB Endowment}, doi = {doi>10.14778/2733004.2733061}, author = {Zohreh Alavi and Lu Zhou and James Powers and Keke Chen} } @inbook {20, title = {Reasoning}, booktitle = {Encyclopedia of Social Network Analysis and Mining}, year = {2014}, pages = {1499{\textendash}1501}, doi = {10.1007/978-1-4614-6170-8_115}, url = {http://dx.doi.org/10.1007/978-1-4614-6170-8_115}, author = {Cong Wang and Pascal Hitzler} } @article {57, title = {Reports on the 2013 AAAI Fall Symposium Series}, journal = {AI Magazine}, volume = {35}, year = {2014}, pages = {69{\textendash}74}, url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2538}, author = {Gully Burns and Yolanda Gil and Yan Liu and Natalia Villanueva-Rosales and Sebastian Risi and Joel Lehman and Jeff Clune and Christian Lebiere and Paul S. Rosenbloom and Frank van Harmelen and James A. Hendler and Pascal Hitzler and Krzysztof Janowicz and Samarth Swarup} } @conference {7, title = {Revisiting default description logics {\textendash} and their role in aligning ontologies}, booktitle = {Semantic Technology, 4th Joint International Conference, JIST 2014}, volume = {8943}, year = {2014}, month = {11/2014}, pages = {3-18}, publisher = {Lecture Notes in Computer Science, Springer}, organization = {Lecture Notes in Computer Science, Springer}, address = {Chiang Mai, Thailand}, abstract = {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.}, keywords = {default logic, defaults, description logics, Ontology Alignment}, doi = {10.1007/978-3-319-15615-6_1}, author = {Kunal Sengupta and Pascal Hitzler and Krzysztof Janowicz}, editor = {T. Supnithi and T. Yamaguchi and Jeff Z. Pan and V. Wuwongse and M. Buranarach} } @conference {287, title = {Semantic Entity Pairing for Improved Data Validation and Discovery}, booktitle = { European Geosciences Union General Assembly 2014, Vienna, Austria, 27 April - 02 May 2014}, volume = {16}, year = {2014}, month = {05/2014}, pages = {2476}, abstract = {

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{\textquoteright}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{\textquoteright}s and R2R{\textquoteright}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.

}, author = {Adam Shepherd and Cynthia Chandler and Robert Arko and Yanning Chen and Adila Krisnadhi and Pascal Hitzler and Thomas Narock and Robert Groman and Shannon Rauch} } @inbook {91, title = {Semantic Web}, booktitle = {Computing Handbook, Third Edition: Computer Science and Software Engineering}, volume = {I}, year = {2014}, pages = {50-1 - 50-13}, publisher = {Chapman and Hall/CRC}, organization = {Chapman and Hall/CRC}, edition = {3}, author = {Pascal Hitzler and Krzysztof Janowicz}, editor = {Allen Tucker and Teofilo Gonzalez and Jorge Diaz-Herrera} } @article {58, title = {Semantic Web and Big Data meets Applied Ontology - The Ontology Summit 2014}, journal = {Applied Ontology}, volume = {9}, year = {2014}, pages = {155{\textendash}170}, doi = {10.3233/AO-140135}, url = {http://dx.doi.org/10.3233/AO-140135}, author = {Leo Obrst and Michael Gr{\"u}ninger and Ken Baclawski and Mike Bennett and Dan Brickley and Gary Berg-Cross and Pascal Hitzler and Krzysztof Janowicz and Christine Kapp and Oliver Kutz and Christoph Lange and Anatoly Levenchuk and Francesca Quattri and Alan Rector and Todd Schneider and Simon Spero and Anne Thessen and Marcela Vegetti and Amanda Vizedom and Andrea Westerinen and Matthew West and Peter Yim} } @article {641, title = {Transmission of data with orthogonal frequency division multiplexing technique for communication networks using GHz frequency band soliton carrier}, journal = {IET Communications}, volume = {8}, year = {2014}, pages = {1364{\textendash}1373}, author = {Amiri, IS and Ebrahimi, Monireh and Yazdavar, Amir Hossein and Ghorbani, S and Alavi, SE and Idrus, Sevia M and Ali, J} } @conference {319, title = {Using Linked Open Data and Semantic Integration to Search Across Geoscience Repositories.}, booktitle = {American Geophysical Union Fall Meeting, San Francisco, 15-19 December 2014}, year = {2014}, author = {Lisa Raymond and Adam Shepherd and Robert Arko and Suzanne Carbotte and Cynthia Chandler and Michelle Cheatham and Douglas Fils and Pascal Hitzler and Krzysztof Janowicz and Matthew Jones and Adila Krisnadhi and Kerstin Lehnert and Audrey Mickle and Thomas Narock and Mark Schildhauer and Peter Wiebe} } @inbook {50, title = {Web Ontology Language (OWL)}, booktitle = {Encyclopedia of Social Network Analysis and Mining}, year = {2014}, pages = {2374{\textendash}2378}, author = {Kunal Sengupta and Pascal Hitzler}, editor = {Reda Alhajj and Jon Rokna} } @conference {134, title = {Is Your Ontology as Hard as You Think? Rewriting Ontologies into Simpler DLs}, booktitle = {Informal Proceedings of the 27th International Workshop on Description Logics, Vienna, Austria, July 17-20, 2014.}, year = {2014}, pages = {128{\textendash}140}, abstract = {

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.

}, keywords = {description logics, OWL, Tractable Reasoning}, url = {http://ceur-ws.org/Vol-1193/paper_75.pdf}, author = {David Carral and Cristina Feier and Ana Armas Romero and Cuenca Grau, Bernardo and Pascal Hitzler and Ian Horrocks} } @conference {94, title = {Automatic Domain Identification for Linked Open Data}, booktitle = {2013 IEEE/WIC/ACM International Conferences on Web Intelligence, WI 2013}, year = {2013}, pages = {205{\textendash}212}, address = {Atlanta, GA, USA}, abstract = {

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.

}, keywords = {dataset search, Domain Identification, Linked Open Data Cloud}, doi = {10.1109/WI-IAT.2013.206}, url = {http://dx.doi.org/10.1109/WI-IAT.2013.206}, author = {Sarasi Lalithsena and Pascal Hitzler and Amit Sheth and Prateek Jain} } @conference {412, title = {Bridging KR and Machine Learning}, booktitle = {Final Report on the 2013 NSF Workshop on Research Challenges and Opportunities in Knowledge Representation}, year = {2013}, address = {Arlington, VA}, author = {Pascal Hitzler and Lise Getoor}, editor = {Natasha Noy and Deborah McGuinness} } @conference {413, title = {Bridging open-world knowledge and closed-world data}, booktitle = {Final Report on the 2013 NSF Workshop on Research Challenges and Opportunities in Knowledge Representation}, year = {2013}, address = {Arlington, VA}, author = {Pascal Hitzler}, editor = {Natasha Noy and Deborah McGuinness} } @article {60, title = {Complexities of Horn Description Logics}, journal = {ACM Trans. Comput. Log.}, volume = {14}, year = {2013}, pages = {2}, abstract = {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.}, keywords = {computational complexity, description logics, Horn logic}, doi = {10.1145/2422085.2422087}, url = {http://doi.acm.org/10.1145/2422085.2422087}, author = {Markus Kr{\"o}tzsch and Sebastian Rudolph and Pascal Hitzler} } @conference {400, title = {Crowdsourcing Semantics for Big Data in Geoscience Applications}, booktitle = {Semantics for Big Data: Papers from the AAAI Symposium}, year = {2013}, month = {11/2013}, address = {Arlington, Virginia}, author = {Tom Narock and Pascal Hitzler}, editor = {Frank van Harmelen and Jim Hendler and Pascal Hitzler and Krzysztof Janowicz} } @conference {10, title = {DistEL: A Distributed EL+ Ontology Classifier}, booktitle = {Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, co-located with the International Semantic Web Conference (ISWC 2013)}, volume = {1046}, year = {2013}, month = {10/2013}, pages = {17-32}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Sydney, Australia}, abstract = {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.}, keywords = {Classification, DistEL, Distributed Reasoning, EL+, OWL, Scalability}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti}, editor = {Thorsten Liebig and Achille Fokoue} } @conference {159, title = {D-SPARQ: Distributed, Scalable and Efficient RDF Query Engine}, booktitle = {Proceedings of the ISWC 2013 Posters \& Demonstrations Track}, volume = {1035}, year = {2013}, pages = {261{\textendash}264}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Sydney, Australia}, abstract = {

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.

}, keywords = {D-SPARQ, Distributed Querying, Scalable RDF querying, SPARQL}, url = {http://ceur-ws.org/Vol-1035/iswc2013_poster_21.pdf}, author = {Raghava Mutharaju and Sherif Sakr and Alessandra Sala and Pascal Hitzler}, editor = {Eva Blomqvist and Tudor Groza} } @conference {155, title = {Editing R2RML Mappings Made Easy}, booktitle = {Proceedings of the ISWC 2013 Posters \& Demonstrations Track, Sydney, Australia, October 23, 2013}, volume = {1035}, year = {2013}, pages = {101{\textendash}104}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, author = {Kunal Sengupta and Peter Haase and Michael Schmidt and Pascal Hitzler}, editor = {Eva Blomqvist and Tudor Groza} } @conference {130, title = {A Geo-ontology Design Pattern for Semantic Trajectories}, booktitle = {Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings}, year = {2013}, pages = {438{\textendash}456}, abstract = {

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.

}, keywords = {Ontology Design Pattern, OWL, Trajectory}, doi = {10.1007/978-3-319-01790-7_24}, url = {http://dx.doi.org/10.1007/978-3-319-01790-7_24}, author = {Yingjie Hu and Krzysztof Janowicz and David Carral and Simon Scheider and Werner Kuhn and Gary Berg-Cross and Pascal Hitzler and Mike Dean and Dave Kolas} } @conference {410, title = {Grand Challenge: From Big Data to Knowledge}, booktitle = { Final Report on the 2013 NSF Workshop on Research Challenges and Opportunities in Knowledge Representation}, year = {2013}, address = {Arlington, VA}, author = {Pascal Hitzler}, editor = {Natasha Noy and Deborah McGuinness} } @conference {409, title = {Knowledge Representation in the Big Data Age.}, booktitle = {NSF Workshop: Research Challenges and Opportunities in Knowledge Representation}, year = {2013}, address = {Arlington, VA}, author = {Pascal Hitzler} } @conference {289, title = {Leveraging Crowdsourcing and Linked Open Data for Geoscience Data Sharing and Discovery}, booktitle = {2013 American Geophysical Union Fall Meeting}, year = {2013}, address = {San Francisco, CA, USA}, author = {Thomas Narock and Eric A. Rozell and Pascal Hitzler and Robert Arko and Cynthia Chandler and Brian D. Wilson} } @conference {411, title = {Lightweight KR}, booktitle = {Final Report on the 2013 NSF Workshop on Research Challenges and Opportunities in Knowledge Representation}, year = {2013}, address = {Arlington, VA}, author = {Pascal Hitzler}, editor = {Natasha Noy and Deborah McGuinness} } @article {63, title = {Linked Data, Big Data, and the 4th Paradigm}, journal = {Semantic Web}, volume = {4}, year = {2013}, pages = {233{\textendash}235}, abstract = {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.}, doi = {10.3233/SW-130117}, url = {http://dx.doi.org/10.3233/SW-130117}, author = {Pascal Hitzler and Krzysztof Janowicz} } @conference {153, title = {Linked Scientometrics: Designing Interactive Scientometrics with Linked Data and Semantic Web Reasoning}, booktitle = {Proceedings of the ISWC 2013 Posters \& Demonstrations Track, Sydney, Australia, October 23, 2013}, volume = {1035}, year = {2013}, pages = {53{\textendash}56}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, author = {Grant McKenzie and Krzysztof Janowicz and Yingjie Hu and Kunal Sengupta and Pascal Hitzler}, editor = {Eva Blomqvist and Tudor Groza} } @conference {49, title = {A Linked-Data-Driven and Semantically-Enabled Journal Portal for Scientometrics}, booktitle = {The Semantic Web - ISWC 2013 - 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21-25, 2013, Proceedings, Part II}, volume = {8219}, year = {2013}, pages = {114{\textendash}129}, publisher = {Springer}, organization = {Springer}, author = {Yingjie Hu and Krzysztof Janowicz and Grant McKenzie and Kunal Sengupta and Pascal Hitzler}, editor = {Harith Alani and Lalana Kagal and Achille Fokoue and Paul T. Groth and Chris Biemann and Josiane Xavier Parreira and Lora Aroyo and Natasha F. Noy and Chris Welty and Krzysztof Janowicz} } @conference {175, title = {Logical linked data compression}, booktitle = {The Semantic Web: Semantics and Big Data.10th Extended Semantic Web Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. }, year = {2013}, pages = {170{\textendash}184}, publisher = {Springer}, organization = {Springer}, author = {Joshi, Amit Krishna and Pascal Hitzler and Dong, Guozhu} } @article {64, title = {The New Manuscript Review System for the Semantic Web Journal}, journal = {Semantic Web}, volume = {4}, year = {2013}, pages = {117}, doi = {10.3233/SW-130095}, url = {http://dx.doi.org/10.3233/SW-130095}, author = {Pascal Hitzler and Krzysztof Janowicz and Kunal Sengupta} } @conference {129, title = {An Ontology Design Pattern for Cartographic Map Scaling}, booktitle = {The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings}, volume = {7882}, year = {2013}, pages = {76{\textendash}93}, publisher = {Springer}, organization = {Springer}, abstract = {

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.

}, keywords = {Map Scaling, Ontology Design Patterns, OWL}, doi = {10.1007/978-3-642-38288-8_6}, url = {http://dx.doi.org/10.1007/978-3-642-38288-8_6}, author = {David Carral and Simon Scheider and Krzysztof Janowicz and Charles Vardeman and Adila Krisnadhi and Pascal Hitzler}, editor = {Philipp Cimiano and {\'O}scar Corcho and Valentina Presutti and Laura Hollink and Sebastian Rudolph} } @conference {154, title = {Optique 1.0: Semantic Access to Big Data: The Case of Norwegian Petroleum Directorate{\textquoteright}s FactPages}, booktitle = {Proceedings of the ISWC 2013 Posters \& Demonstrations Track, Sydney, Australia, October 23, 2013}, volume = {1035}, year = {2013}, pages = {65{\textendash}68}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, author = {Evgeny Kharlamov and Martin Giese and Ernesto Jim{\'e}nez-Ruiz and Martin G. Skj{\ae}veland and Ahmet Soylu and Dmitriy Zheleznyakov and Timea Bagosi and Marco Console and Peter Haase and Ian Horrocks and Sarunas Marciuska and Christoph Pinkel and Mariano Rodriguez-Muro and Marco Ruzzi and Valerio Santarelli and Domenico Fabio Savo and Kunal Sengupta and Michael Schmidt and Evgenij Thorstensen and Johannes Trame and Arild Waaler}, editor = {Eva Blomqvist and Tudor Groza} } @article {61, title = {Paraconsistent OWL and Related Logics}, journal = {Semantic Web}, volume = {4}, year = {2013}, pages = {395{\textendash}427}, abstract = {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.}, keywords = {Automated Deduction, Complexity, Description Logic, OWL, Paraconsistency, Semantic Web, Web Ontology Language}, doi = {10.3233/SW-2012-0066}, url = {http://dx.doi.org/10.3233/SW-2012-0066}, author = {Frederick Maier and Yue Ma and Pascal Hitzler} } @proceedings {311, title = {Proceedings of the Ninth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy{\textquoteright}13, at the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 2013}, journal = {Ninth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy{\textquoteright}13, at the 23rd International Joint Conference on Artificial Intelligence}, year = {2013}, address = {Beijing, China}, editor = {Artur S. d{\textquoteright}Avila Garcez and Pascal Hitzler and Lu{\'\i}s C. Lamb} } @article {62, title = {Reasoning with Inconsistencies in Hybrid MKNF Knowledge Bases}, journal = {Logic Journal of the IGPL}, volume = {21}, year = {2013}, pages = {263{\textendash}290}, abstract = {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.}, keywords = {Data complexity, Description logics and rules, Knowledge representation, Non-monotonic reasoning, Paraconsistent reasoning}, doi = {10.1093/jigpal/jzs043}, url = {http://dx.doi.org/10.1093/jigpal/jzs043}, author = {Shasha Huang and Qingguo Li and Pascal Hitzler} } @conference {164, title = {Scale reasoning with fuzzy-EL+ ontologies based on MapReduce}, booktitle = {Proceedings of the IJCAI-2013 Workshop on Weighted Logics for Artificial Intelligence (WL4AI 2013)}, year = {2013}, pages = {87-93}, address = {Beijing, China}, abstract = {

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.

}, author = {Zhangquan Zhou and Guilin Qi and Chang Liu and Pascal Hitzler and Raghava Mutharaju}, editor = {Lluis Godo and Henri Prade and Guilin Qi} } @proceedings {310, title = {Semantics for Big Data: Papers from the AAAI Symposium, November 15-17, 2013, Arlington, Virginia}, journal = {AAAI Symposium on Semantics for Big Data}, year = {2013}, address = {Arlington, Virginia, USA}, editor = {Frank van Harmelen and James A. Hendler and Pascal Hitzler and Krzysztof Janowicz} } @mastersthesis {643, title = {Side Effects Recognition as Implicit Opinion Words in Drug Reviews}, year = {2013}, school = {Universiti Teknologi Malaysia}, type = {Master}, author = {Ebrahimi, Monireh} } @conference {21, title = {SROIQ Syntax Approximation by Using Nominal Schemas}, booktitle = {Informal Proceedings of the 26th International Workshop on Description Logics, Ulm, Germany, July 23 - 26, 2013}, year = {2013}, pages = {988{\textendash}999}, url = {http://ceur-ws.org/Vol-1014/paper_31.pdf}, author = {Cong Wang and David Carral and Pascal Hitzler} } @conference {101, title = {String Similarity Metrics for Ontology Alignment}, booktitle = {International Semantic Web Conference}, year = {2013}, publisher = {Springer}, organization = {Springer}, address = {Sydney, Australia}, abstract = {

}, author = {Michelle Cheatham and Pascal Hitzler} } @conference {103, title = {StringsAuto and MapSSS Results for OAEI 2013}, booktitle = {8th International Workshop on Ontology Matching}, year = {2013}, address = {Sydney, Australia}, author = {Michelle Cheatham and Pascal Hitzler} } @article {308, title = {There{\textquoteright}s No Money in Linked Data}, year = {2013}, institution = {DaSe Lab, Department of Computer Science and Engineering, Wright State University}, address = {Dayton, OH, USA}, abstract = {

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.

}, author = {Prateek Jain and Pascal Hitzler and Krzysztof Janowicz and Chitra Venkatramani} } @conference {401, title = {Thoughts on the Complex Relation Between Linked Data, Semantic Annotations, and Ontologies}, booktitle = {ESAIR{\textquoteright}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}, year = {2013}, pages = {41{\textendash}44}, publisher = {ACM}, organization = {ACM}, address = {San Francisco, CA}, abstract = {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.}, doi = {10.1145/2513204.2513218}, url = {http://doi.acm.org/10.1145/2513204.2513218}, author = {Krzysztof Janowicz and Pascal Hitzler}, editor = {Paul N. Bennett and Evgeniy Gabrilovich and Jaap Kamps and Jussi Karlgren} } @conference {128, title = {Towards an Efficient Algorithm to Reason over Description Logics Extended with Nominal Schemas}, booktitle = {Web Reasoning and Rule Systems - 7th International Conference, {RR} 2013, Mannheim, Germany, July 27-29, 2013. Proceedings}, year = {2013}, pages = {65{\textendash}79}, abstract = {

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.

}, keywords = {description logics, EL++, Nominal Schemas}, doi = {10.1007/978-3-642-39666-3_6}, url = {http://dx.doi.org/10.1007/978-3-642-39666-3_6}, author = {David Carral and Cong Wang and Pascal Hitzler} } @book {290, title = {语义Web技术基础}, year = {2013}, publisher = {Tsinghua University Press}, organization = {Tsinghua University Press}, author = {Pascal Hitzler and Markus Kr{\"o}tzsch and Sebastian Rudolph} } @inbook {174, title = {Alignment-based querying of linked open data}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2012}, year = {2012}, pages = {807{\textendash}824}, publisher = {Springer}, organization = {Springer}, author = {Joshi, Amit Krishna and Prateek Jain and Pascal Hitzler and Peter Z. Yeh and Kunal Verma and Amit Sheth and Mariana Damova} } @article {68, title = {Cognitive Approaches for the Semantic Web (Dagstuhl Seminar 12221)}, journal = {Dagstuhl Reports}, volume = {2}, year = {2012}, pages = {93{\textendash}116}, doi = {10.4230/DagRep.2.5.93}, url = {http://dx.doi.org/10.4230/DagRep.2.5.93}, author = {Dedre Gentner and Frank van Harmelen and Pascal Hitzler and Krzysztof Janowicz and Kai-Uwe K{\"u}hnberger} } @conference {23, title = {Consequence-Based Procedure for Description Logics with Self-Restriction}, booktitle = {Semantic Web and Web Science - 6th Chinese Semantic Web Symposium and 1st Chinese Web Science Conference, CSWS 2012, Shenzhen, China, November 28-30, 2012.}, year = {2012}, pages = {169{\textendash}180}, doi = {10.1007/978-1-4614-6880-6_15}, url = {http://dx.doi.org/10.1007/978-1-4614-6880-6_15}, author = {Cong Wang and Pascal Hitzler} } @article {66, title = {The Digital Earth as Knowledge Engine}, journal = {Semantic Web}, volume = {3}, year = {2012}, pages = {213{\textendash}221}, abstract = {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.}, doi = {10.3233/SW-2012-0070}, url = {http://dx.doi.org/10.3233/SW-2012-0070}, author = {Krzysztof Janowicz and Pascal Hitzler} } @conference {127, title = {Extending Description Logic Rules}, booktitle = {The Semantic Web: Research and Applications - 9th Extended Semantic Web Conference, ESWC 2012, Heraklion, Crete, Greece, May 27-31, 2012. Proceedings}, year = {2012}, pages = {345{\textendash}359}, abstract = {

Description Logics {\textendash} the logics underpinning the Web Ontology Language OWL {\textendash} 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.

}, keywords = {description logics, OWL, Rules}, doi = {10.1007/978-3-642-30284-8_30}, url = {http://dx.doi.org/10.1007/978-3-642-30284-8_30}, author = {David Carral and Pascal Hitzler} } @conference {166, title = {How I Would Like Semantic Web To Be, For My Children}, booktitle = {What will the Semantic Web look like 10 years from now? co-located with the 11th International Semantic Web Conference 2012 (ISWC 2012)}, year = {2012}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Boston, MA, USA}, abstract = {

Semantic Web, since its inception, has gone through lot of developments in its relatively nascent existence; right from people{\textquoteright}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.

}, author = {Raghava Mutharaju} } @conference {125, title = {Integrating {OWL} and Rules: A Syntax Proposal for Nominal Schemas}, booktitle = {Proceedings of OWL: Experiences and Directions Workshop 2012, Heraklion, Crete, Greece, May 27-28, 2012}, volume = {849}, year = {2012}, month = {05/2012}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {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 {\textquotedblleft}variable nominal classes{\textquotedblright} 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.}, url = {http://ceur-ws.org/Vol-849/paper_6.pdf}, author = {David Carral and Adila Krisnadhi and Pascal Hitzler}, editor = {Pavel Klinov and Matthew Horridge} } @conference {402, title = {Key Ingredients For Your Next Semantics Elevator Talk}, booktitle = {Advances in Conceptual Modeling - {ER} 2012 Workshops CMS, ECDM-NoCoDA, MoDIC, MORE-BI, RIGiM, SeCoGIS, WISM, Florence, Italy, October 15-18, 2012. Proceedings}, volume = {7518}, year = {2012}, pages = {213{\textendash}220}, publisher = {Springer}, organization = {Springer}, address = {Florence, Italy}, abstract = {

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.

}, doi = {10.1007/978-3-642-33999-8_27}, url = {http://dx.doi.org/10.1007/978-3-642-33999-8_27}, author = {Krzysztof Janowicz and Pascal Hitzler}, editor = {Silvana Castano and Panos Vassiliadis and Laks V. S. Lakshmanan and Mong-Li Lee} } @conference {170, title = {Konf Connect}, booktitle = {Metadata Challenge at the 21st International Conference on World Wide Web (WWW 2012)}, year = {2012}, address = {Lyon, France}, abstract = {

We present an application called Konf-Connect to improve\ the conference attending experience of the people who attend a conference. This tool provides search facilities to nd people with similar\ interests. The application makes use of Semantic Web dog food dataset\ to gather information regarding the conference at hand. This is helpful for people attending the conference who are looking for networking\ opportunities with people having expertise in the specic areas of interest. The application can also be extended to be used as general purpose\ expert search system.

}, author = {David Carral and Joshi, Amit Krishna and Adila Krisnadhi and Raghava Mutharaju and Kunal Sengupta and Cong Wang} } @conference {126, title = {A logical geo-ontology design pattern for quantifying over types}, booktitle = {SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems (formerly known as GIS), SIGSPATIAL{\textquoteright}12, Redondo Beach, CA, USA, November 7-9, 2012}, year = {2012}, pages = {239{\textendash}248}, keywords = {Biodiversity, description logics, Ontology Design Patterns, OWL}, doi = {10.1145/2424321.2424352}, url = {http://doi.acm.org/10.1145/2424321.2424352}, author = {David Carral and Krzysztof Janowicz and Pascal Hitzler} } @conference {97, title = {Moving beyond SameAs with PLATO: Partonomy detection for Linked Data}, booktitle = {23rd ACM Conference on Hypertext and Social Media, HT {\textquoteright}12}, year = {2012}, pages = {33{\textendash}42}, publisher = {ACM}, organization = {ACM}, address = {Milwaukee, WI, USA}, abstract = {

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.

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.

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.

}, keywords = {Linked Open Data Cloud, Mereology, Part of Relation}, doi = {10.1145/2309996.2310004}, url = {http://doi.acm.org/10.1145/2309996.2310004}, author = {Prateek Jain and Pascal Hitzler and Kunal Verma and Peter Z. Yeh and Amit Sheth}, editor = {Ethan V. Munson and Markus Strohmaier} } @article {71, title = {Open and transparent: the review process of the Semantic Web journal}, journal = {Learned Publishing}, volume = {25}, year = {2012}, pages = {48-55}, abstract = {

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.

}, author = {Krzysztof Janowicz and Pascal Hitzler} } @article {303, title = {OWL 2 Web Ontology Language: Primer (Second Edition)}, year = {2012}, month = {12/11/2012}, pages = {W3C Recommendation}, url = {http://www.w3.org/TR/owl2-primer}, author = {Pascal Hitzler and Markus Kr{\"o}tzsch and Bijan Parsia and Peter F. Patel-Schneider and Sebastian Rudolph} } @proceedings {312, title = {Proceedings of the Eighth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy{\textquoteright}12, at the 26th Conference on Artificial Intelligence, AAAI-12, Toronto, Canada, July 2012}, journal = {Eighth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy{\textquoteright}12, at the 26th Conference on Artificial Intelligence, AAAI-12}, year = {2012}, address = {Toronto, Canada}, editor = {Artur S. d{\textquoteright}Avila Garcez and Pascal Hitzler and Lu{\'\i}s C. Lamb} } @article {216, title = {Reasoning Approaches for Nominal Schemas}, volume = {Poster and Demonstration Proceedings}, year = {2012}, publisher = {JIST}, address = {Nara, Japan}, author = {Cong Wang and Adila Krisnadhi and David Carral and Pascal Hitzler} } @conference {96, title = {Reasoning with Fuzzy-EL+ Ontologies Using MapReduce}, booktitle = {ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track}, volume = {242}, year = {2012}, pages = {933{\textendash}934}, publisher = {IOS Press}, organization = {IOS Press}, address = {Montpellier, France}, abstract = {

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.

}, doi = {10.3233/978-1-61499-098-7-933}, url = {http://dx.doi.org/10.3233/978-1-61499-098-7-933}, author = {Zhangquan Zhou and Guilin Qi and Chang Liu and Pascal Hitzler and Raghava Mutharaju}, editor = {Luc De Raedt and Christian Bessi{\`e}re and Didier Dubois and Patrick Doherty and Paolo Frasconi and Fredrik Heintz and Peter J. F. Lucas} } @conference {124, title = {Recent Advances in Integrating {OWL} and Rules}, booktitle = {Web Reasoning and Rule Systems - 6th International Conference, RR 2012, Vienna, Austria, September 10-12, 2012. Proceedings}, volume = {7497}, year = {2012}, month = {09/2012}, pages = {225-228}, publisher = {Springer}, organization = {Springer}, address = {Austria, Vienna}, abstract = {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.}, keywords = {description logics, OWL, Rules}, doi = {10.1007/978-3-642-33203-6_20}, url = {http://dx.doi.org/10.1007/978-3-642-33203-6_20}, author = {Matthias Knorr and David Carral and Pascal Hitzler and Adila Krisnadhi and Frederick Maier and Cong Wang}, editor = {Markus Kr{\"o}tzsch and Umberto Straccia} } @conference {95, title = {Reconciling OWL and Non-monotonic Rules for the Semantic Web}, booktitle = {ECAI 2012 - 20th European Conference on Artificial Intelligence. Including Prestigious Applications of Artificial Intelligence (PAIS-2012) System Demonstrations Track}, volume = {242}, year = {2012}, pages = {474{\textendash}479}, publisher = {IOS Press}, organization = {IOS Press}, address = {Montpellier, France}, abstract = {

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 {\textquotedblleft}look and feel{\textquotedblright} of a description logic and avoids hybrid or first-order syntaxes.

}, doi = {10.3233/978-1-61499-098-7-474}, url = {http://dx.doi.org/10.3233/978-1-61499-098-7-474}, author = {Matthias Knorr and Pascal Hitzler and Frederick Maier}, editor = {Luc De Raedt and Christian Bessi{\`e}re and Didier Dubois and Patrick Doherty and Paolo Frasconi and Fredrik Heintz and Peter J. F. Lucas} } @article {67, title = {Reports of the AAAI 2012 Conference Workshops}, journal = {AI Magazine}, volume = {33}, year = {2012}, pages = {119{\textendash}127}, url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2444}, author = {Vikas Agrawal and Jorge Baier and Kostas E. Bekris and Yiling Chen and Artur S. d{\textquoteright}Avila Garcez and Pascal Hitzler and Patrik Haslum and Dietmar Jannach and Edith Law and Freddy L{\'e}cu{\'e} and Lu{\'\i}s C. Lamb and Cynthia Matuszek and H{\'e}ctor Palacios and Biplav Srivastava and Lokendra Shastri and Nathan R. Sturtevant and Roni Stern and Stefanie Tellex and Stavros Vassos} } @article {72, title = {Reports of the AAAI 2012 Conference Workshops}, journal = {AI Magazine}, volume = {33}, year = {2012}, pages = {119-127}, url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2444}, author = {Vikas Agrawal and Jorge Baier and Kostas E. Bekris and Yiling Chen and Artur S. d{\textquoteright}Avila Garcez and Pascal Hitzler and Patrik Haslum and Dietmar Jannach and Edith Law and Freddy L{\'e}cu{\'e} and Lu{\'\i}s C. Lamb and Cynthia Matuszek and H{\'e}ctor Palacios and Biplav Srivastava and Lokendra Shastri and Nathan R. Sturtevant and Roni Stern and Stefanie Tellex and Stavros Vassos} } @conference {22, title = {A Resolution Procedure for Description Logics with Nominal Schemas}, booktitle = {Semantic Technology, Second Joint International Conference, JIST 2012, Nara, Japan, December 2-4, 2012. Proceedings}, year = {2012}, pages = {1{\textendash}16}, doi = {10.1007/978-3-642-37996-3_1}, url = {http://dx.doi.org/10.1007/978-3-642-37996-3_1}, author = {Cong Wang and Pascal Hitzler} } @article {307, title = {Semantic Aspects of EarthCube}, year = {2012}, abstract = {

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.

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.

}, author = {Pascal Hitzler and Krzysztof Janowicz and Gary Berg-Cross and Leo Obrst and Amit Sheth and Timothy Finin and Isabel Cruz} } @conference {403, title = {Semantics and Ontologies for EarthCube}, booktitle = {Workshop on GIScience in the Big Data Age, In conjunction with the seventh International Conference on Geographic Information Science 2012 (GIScience 2012)}, year = {2012}, address = {Columbus, Ohio, USA}, abstract = {

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.

}, author = {Gary Berg-Cross and Isabel Cruz and Mike Dean and Tim Finin and Mark Gahegan and Pascal Hitzler and Hook Hua and Krzysztof Janowicz and Naicong Li and Philip Murphy and Bryce Nordgren and Leo Obrst and Mark Schildhauer and Amit Sheth and Krishna Sinha and Anne Thessen and Nancy Wiegand and Ilya Zaslavsky}, editor = {Krzysztof Janowicz and C. Kessler and T. Kauppinen and Dave Kolas and Simon Scheider} } @conference {19, title = {A Tableau Algorithm for Description Logics with Nominal Schemas}, booktitle = {Web Reasoning and Rule Systems, 6th International Conference, RR2012, Vienna, Austria, September 10-12, 2012, Proceedings}, volume = {7497}, year = {2012}, month = {09/2012}, pages = {234-237}, publisher = {Springer}, organization = {Springer}, abstract = {

We present a tableau algorithm for the description logic\ ALCOV. This description logic is obtained by extending the description\ logic ALCO with the expressive nominal schema construct that enables\ DL-safe datalog with predicates of arbitrary arity to be covered within\ the description logic framework. The tableau algorithm provides a basis to implement a delayed grounding strategy which was not facilitated\ by earlier versions of decision procedures for satisfiability in expressive\ description logics with nominal schemas.

}, doi = {10.1007/978-3-642-33203-6_22}, author = {Adila Krisnadhi and Pascal Hitzler}, editor = {Markus Kr{\"o}tzsch and Umberto Straccia} } @conference {176, title = {Towards logical linked data compression}, booktitle = {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}, year = {2012}, author = {Joshi, Amit Krishna and Pascal Hitzler and Dong, Guozhu} } @article {65, title = {Type-Elimination-Based Reasoning for the Description Logic SHIQbs using Decision Diagrams and Disjunctive Datalog}, journal = {Logical Methods in Computer Science}, volume = {8}, year = {2012}, abstract = {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.}, keywords = {datalog, decision diagrams, description logics, type elimination}, doi = {10.2168/LMCS-8(1:12)2012}, url = {http://dx.doi.org/10.2168/LMCS-8(1:12)2012}, author = {Sebastian Rudolph and Markus Kr{\"o}tzsch and Pascal Hitzler} } @conference {160, title = {Very Large Scale OWL Reasoning through Distributed Computation}, booktitle = {11th International Semantic Web Conference (ISWC 2012), Proceedings, Part II}, volume = {7650}, year = {2012}, pages = {407{\textendash}414}, publisher = {Springer}, organization = {Springer}, address = {Boston, MA, USA}, abstract = {

Due to recent developments in reasoning algorithms of the\ various OWL profiles, the classification time for an ontology has come\ down drastically. For all of the popular reasoners, in order to process\ an ontology, an implicit assumption is that the ontology should fit in\ primary memory. The memory requirements for a reasoner are already\ quite high, and considering the ever increasing size of the data to be\ processed and the goal of making reasoning Web scale, this assumption\ becomes overly restrictive. In our work, we study several distributed\ 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.

}, keywords = {Distributed Reasoning, Ontology Classification, OWL EL}, doi = {10.1007/978-3-642-35173-0_30}, url = {http://dx.doi.org/10.1007/978-3-642-35173-0_30}, author = {Raghava Mutharaju}, editor = {Philippe Cudr{\'e}-Mauroux and Jeff Heflin and Evren Sirin and Tania Tudorache and J{\'e}r{\^o}me Euzenat and Manfred Hauswirth and Josiane Xavier Parreira and James A. Hendler and Guus Schreiber and Abraham Bernstein and Eva Blomqvist} } @conference {139, title = {A Better Uncle for {OWL}: Nominal Schemas for Integrating Rules and Ontologies}, booktitle = {Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, March 28 - April 1, 2011}, year = {2011}, month = {03/2011}, pages = {645-654}, publisher = {ACM}, organization = {ACM}, abstract = {We propose a description-logic style extension of OWL 2 with nominal schemas which can be used like "variable nominal classes" 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.}, keywords = {datalog, Description Logic, Semantic Web Rule Language, SROIQ, tractability, Web Ontology Language}, isbn = {978-1-4503-0632-4}, doi = {10.1145/1963405.1963496}, url = {http://doi.acm.org/10.1145/1963405.1963496}, author = {Markus Kr{\"o}tzsch and Frederick Maier and Adila Krisnadhi and Pascal Hitzler}, editor = {Sadagopan Srinivasan and Krithi Ramamritham and Arun Kumar and M. P. Ravindra and Elisa Bertino and Ravi Kumar} } @article {73, title = {Computing Inconsistency Measure based on Paraconsistent Semantics}, journal = {Journal of Logic and Computation}, volume = {21}, year = {2011}, pages = {1257{\textendash}1281}, abstract = {

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.

}, doi = {10.1093/logcom/exq053}, url = {http://dx.doi.org/10.1093/logcom/exq053}, author = {Yue Ma and Guilin Qi and Pascal Hitzler} } @conference {99, title = {Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton}, booktitle = {The Semantic Web: Research and Applications - 8th Extended Semantic Web Conference, ESWC 2011}, volume = {6643}, year = {2011}, pages = {80{\textendash}92}, publisher = {Springer}, organization = {Springer}, address = {Heraklion, Crete, Greece}, abstract = {

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 {\textendash} 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) {\textendash} 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.

}, doi = {10.1007/978-3-642-21034-1_6}, url = {http://dx.doi.org/10.1007/978-3-642-21034-1_6}, author = {Prateek Jain and Peter Z. Yeh and Kunal Verma and Reymonrod G. Vasquez and Mariana Damova and Pascal Hitzler and Amit Sheth}, editor = {Grigoris Antoniou and Marko Grobelnik and Elena Paslaru Bontas Simperl and Bijan Parsia and Dimitris Plexousakis and Pieter De Leenheer and Jeff Z. Pan} } @article {74, title = {Local Closed World Reasoning with Description Logics under the Well-Founded Semantics}, journal = {Artificial Intelligence}, volume = {175}, year = {2011}, pages = {1528{\textendash}1554}, abstract = {

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.

}, keywords = {Description Logic, Knowledge representation, Logic Programming, Non-monotonic reasoning, Ontologies, Semantic Web}, doi = {10.1016/j.artint.2011.01.007}, url = {http://dx.doi.org/10.1016/j.artint.2011.01.007}, author = {Matthias Knorr and Jos{\'e} J{\'u}lio Alferes and Pascal Hitzler} } @conference {141, title = {Local Closed World Semantics: Grounded Circumscription for Description Logics}, booktitle = {Web Reasoning and Rule Systems - 5th International Conference, RR 2011, Galway, Ireland, August 29-30, 2011. Proceedings}, volume = {6902}, year = {2011}, month = {08/2011}, pages = {263-268}, publisher = {Springer}, organization = {Springer}, abstract = {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.}, isbn = {978-3-642-23579-5}, doi = {10.1007/978-3-642-23580-1}, url = {http://dx.doi.org/10.1007/978-3-642-23580-1}, author = {Adila Krisnadhi and Kunal Sengupta and Pascal Hitzler}, editor = {Sebastian Rudolph and Claudio Gutierrez} } @conference {48, title = {Local Closed World Semantics: Grounded Circumscription for {OWL}}, booktitle = {The Semantic Web - ISWC 2011 - 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part I}, volume = {7031}, year = {2011}, month = {10/2011}, pages = {617-632}, publisher = {Springer}, organization = {Springer}, abstract = {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.}, doi = {10.1007/978-3-642-25073-6_39}, author = {Kunal Sengupta and Adila Krisnadhi and Pascal Hitzler}, editor = {Lora Aroyo and Chris Welty and Harith Alani and Jamie Taylor and Abraham Bernstein and Lalana Kagal and Natasha F. Noy and Eva Blomqvist} } @conference {143, title = {Local Closed World Semantics: Keep it simple, stupid!}, booktitle = {Proceedings of the 24th International Workshop on Description Logics (DL 2011), Barcelona, Spain, July 13-16, 2011}, volume = {745}, year = {2011}, month = {07/2011}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {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.}, keywords = {circumscription, closed world, decidability, Description Logic}, url = {http://ceur-ws.org/Vol-745/paper_12.pdf}, author = {Adila Krisnadhi and Kunal Sengupta and Pascal Hitzler}, editor = {Riccardo Rosati and Sebastian Rudolph and Michael Zakharyaschev} } @book {291, title = {Logik und Logikprogrammierung Band 2: Aufgaben und L{\"o}sungen}, year = {2011}, publisher = {Synchron Verlag}, organization = {Synchron Verlag}, address = {Heidelberg, Germany}, issn = {978-3-935025-85-0}, author = {Steffen H{\"o}lldobler and Sebastian Bader and Bertram Fronh{\"o}fer and Ursula Hans and Pascal Hitzler and Markus Kr{\"o}tzsch and Tobias Pietzsch} } @conference {104, title = {MapSSS Results for OAEI 2011}, booktitle = {6th International Workshop on Ontology Matching}, year = {2011}, address = {Bonn, Germany}, author = {Michelle Cheatham} } @proceedings {296, title = {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}, journal = {Confederated International Conferences, CoopIS, DOA-SVI, and ODBASE 2011}, volume = {7044}, year = {2011}, publisher = {Springer}, address = {Hersonissos, Crete, Greece}, editor = {Robert Meersman and Tharam S. Dillon and Pilar Herrero and Akhil Kumar and Manfred Reichert and Li Qing and Beng Chin Ooi and Ernesto Damiani and Douglas C. Schmidt and Jules White and Manfred Hauswirth and Pascal Hitzler and Mukesh K. Mohania} } @proceedings {297, title = {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}, journal = {Confederated International Conferences, CoopIS, DOA-SVI, and ODBASE 2011}, volume = {7045}, year = {2011}, publisher = {Springer}, address = {Hersonissos, Crete, Greece}, editor = {Robert Meersman and Tharam S. Dillon and Pilar Herrero and Akhil Kumar and Manfred Reichert and Li Qing and Beng Chin Ooi and Ernesto Damiani and Douglas C. Schmidt and Jules White and Manfred Hauswirth and Pascal Hitzler and Mukesh K. Mohania} } @conference {142, title = {Nominal Schemas for Integrating Rules and Description Logics}, booktitle = {Proceedings of the 24th International Workshop on Description Logics (DL 2011), Barcelona, Spain, July 13-16, 2011}, volume = {745}, year = {2011}, month = {07/2011}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {We propose an extension of SROIQ with nominal schemas which can be used like {\textquotedblleft}variable nominal concepts{\textquotedblright} 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.}, url = {http://ceur-ws.org/Vol-745/paper_39.pdf}, author = {Markus Kr{\"o}tzsch and Frederick Maier and Adila Krisnadhi and Pascal Hitzler}, editor = {Riccardo Rosati and Sebastian Rudolph and Michael Zakharyaschev} } @conference {140, title = {{OWL} and Rules}, booktitle = {Reasoning Web. Semantic Technologies for the Web of Data - 7th International Summer School 2011, Galway, Ireland, August 23-27, 2011, Tutorial Lectures}, volume = {6848}, year = {2011}, month = {08/2011}, pages = {382-415}, publisher = {Springer}, organization = {Springer}, abstract = {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. }, isbn = {978-3-642-23031-8}, doi = {10.1007/978-3-642-23032-5}, url = {http://dx.doi.org/10.1007/978-3-642-23032-5}, author = {Adila Krisnadhi and Frederick Maier and Pascal Hitzler}, editor = {Axel Polleres and Claudia d{\textquoteright}Amato and Marcelo Arenas and Siegfried Handschuh and Paula Kroner and Sascha Ossowski and Peter F. Patel-Schneider} } @conference {98, title = {Paraconsistent Semantics for Hybrid MKNF Knowledge Bases}, booktitle = {Web Reasoning and Rule Systems - 5th International Conference, RR 2011}, volume = {6902}, year = {2011}, pages = {93{\textendash}107}, publisher = {Springer}, organization = {Springer}, address = {Galway, Ireland}, abstract = {

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.

}, doi = {10.1007/978-3-642-23580-1_8}, url = {http://dx.doi.org/10.1007/978-3-642-23580-1_8}, author = {Shasha Huang and Qingguo Li and Pascal Hitzler}, editor = {Sebastian Rudolph and Claudio Gutierrez} } @proceedings {313, title = {Proceedings of the Seventh International Workshop on Neural-Symbolic Learning and Reasoning, NeSy{\textquoteright}11, at the 22nd International Joint Conference on Artificial Intelligence, IJCAI-11, Barcelona, Catalonia (Spain), 2011}, journal = {Seventh International Workshop on Neural-Symbolic Learning and Reasoning, NeSy{\textquoteright}11, at the 22nd International Joint Conference on Artificial Intelligence, IJCAI-11}, volume = {764}, year = {2011}, publisher = {CEUR-WS.org}, address = {Barcelona, Catalonia, Spain}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-764}, editor = {Artur S. d{\textquoteright}Avila Garcez and Pascal Hitzler and Lu{\'\i}s C. Lamb} } @conference {404, title = {Representation of Parsimonious Covering Theory in OWL-DL}, booktitle = {Proceedings of the 8th International Workshop on OWL: Experiences and Directions {(OWLED} 2011), San Francisco, California, USA, June 5-6, 2011}, volume = {796}, year = {2011}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {San Francisco, California, USA}, abstract = {

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.

}, author = {Cory A. Henson and Krishnaprasad Thirunarayan and Amit P. Sheth and Pascal Hitzler}, editor = {Michel Dumontier and M{\'e}lanie Courtot} } @article {75, title = {Semantic Web surveys and applications}, journal = {Semantic Web}, volume = {2}, year = {2011}, pages = {65{\textendash}66}, doi = {10.3233/SW-2011-0047}, url = {http://dx.doi.org/10.3233/SW-2011-0047}, author = {Pascal Hitzler and Krzysztof Janowicz} } @article {76, title = {Semantic Web tools and systems}, journal = {Semantic Web}, volume = {2}, year = {2011}, pages = {1{\textendash}2}, doi = {10.3233/SW-2011-0035}, url = {http://dx.doi.org/10.3233/SW-2011-0035}, author = {Pascal Hitzler and Krzysztof Janowicz} } @conference {305, title = {Web Reasoning and Rule Systems: Five Years into the Conference}, booktitle = {ALP Newsletter, Association of Logic Programming}, year = {2011}, month = {12/2011}, abstract = {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{\textquoteright}s attention to the next event in the series, which will take place in Vienna in September 2012.}, url = {http://www.cs.nmsu.edu/ALP/2011/12/web-reasoning-and-rule-systems-five-years-into-the-conference}, author = {Francesco Calimeri and Pascal Hitzler} } @conference {100, title = {What{\textquoteright}s Happening in Semantic Web - ... and What FCA Could Have to Do with It}, booktitle = {Formal Concept Analysis - 9th International Conference, ICFCA 2011}, volume = {6628}, year = {2011}, pages = {18{\textendash}23}, publisher = {Springer}, organization = {Springer}, address = {Nicosia, Cyprus}, doi = {10.1007/978-3-642-20514-9_2}, url = {http://dx.doi.org/10.1007/978-3-642-20514-9_2}, author = {Pascal Hitzler}, editor = {Petko Valtchev and Robert J{\"a}schke} } @proceedings {314, title = {10302 Abstracts Collection - Learning paradigms in dynamic environments}, journal = {Learning paradigms in dynamic environments}, year = {2010}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany}, address = {Dagstuhl, Germany}, keywords = {Autonomous learning, Dynamic systems, Neural-symbolic integration, Neurobiology, Recurrent neural networks, Speech processing}, issn = {1862-4405}, url = {http://drops.dagstuhl.de/opus/volltexte/2010/2804}, editor = {Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint} } @conference {405, title = {10302 Summary {\textendash} Learning paradigms in dynamic environments}, booktitle = {Learning paradigms in dynamic environments}, number = {10302}, year = {2010}, publisher = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany}, organization = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany}, address = {Dagstuhl, Germany}, abstract = {

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.

}, url = {http://drops.dagstuhl.de/opus/volltexte/2010/2802}, author = {Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint}, editor = {Barbara Hammer and Pascal Hitzler and Wolfgang Maass and Marc Toussaint} } @conference {113, title = {Approximate Instance Retrieval on Ontologies}, booktitle = {Database and Expert Systems Applications, 21st International Conference, DEXA 2010}, volume = {6261}, year = {2010}, pages = {503{\textendash}511}, publisher = {Springer}, organization = {Springer}, address = {Bilbao, Spain}, abstract = {

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.

}, doi = {10.1007/978-3-642-15364-8_43}, url = {http://dx.doi.org/10.1007/978-3-642-15364-8_43}, author = {Tuvshintur Tserendorj and Stephan Grimm and Pascal Hitzler}, editor = {Pablo Garcia Bringas and Abdelkader Hameurlain and Gerald Quirchmayr} } @article {83, title = {Computational Complexity and Anytime Algorithm for Inconsistency Measurement}, journal = {International Journal of Software and Informatics}, volume = {4}, year = {2010}, pages = {3{\textendash}21}, abstract = {

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

}, keywords = {algorithm, computational complexity, inconsistency measurement, Knowledge representation, multi-valued logic}, url = {http://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41\&flag=1}, author = {Yue Ma and Guilin Qi and Guohui Xiao and Pascal Hitzler and Zuoquan Lin} } @article {80, title = {Concept learning in description logics using refinement operators}, journal = {Machine Learning}, volume = {78}, year = {2010}, pages = {203{\textendash}250}, abstract = {

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.

}, keywords = {description logics, Inductive logic programming, OWL, refinement operators, Semantic Web, Structured Machine Learning}, url = {http://springerlink.metapress.com/content/c040n45u15qrnu44/}, author = {Jens Lehmann and Pascal Hitzler} } @conference {407, title = {Distance-based Measures of Inconsistency and Incoherency for Description Logics}, booktitle = {Proceedings of the 23rd International Workshop on Description Logics (DL2010)}, volume = {573}, year = {2010}, pages = {475-485}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Waterloo, Canada}, abstract = {

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.

}, author = {Yue Ma and Pascal Hitzler}, editor = {Volker Haarslev and David Toman and Grant Weddell} } @article {167, title = {Distributed Reasoning with EL++ Using MapReduce}, year = {2010}, institution = {Wright State University}, address = {Dayton, OH, USA}, abstract = {

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.

}, author = {Frederick Maier and Raghava Mutharaju and Pascal Hitzler} } @article {81, title = {Extracting Reduced Logic Programs from Artificial Neural Networks}, journal = {Applied Intelligence}, volume = {32}, year = {2010}, pages = {249{\textendash}266}, abstract = {

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 {\textemdash} where simple is being understood in some clearly defined and meaningful way.

}, doi = {10.1007/s10489-008-0142-y}, url = {http://dx.doi.org/10.1007/s10489-008-0142-y}, author = {Jens Lehmann and Sebastian Bader and Pascal Hitzler} } @conference {408, title = {Flexible Bootstrapping-Based Ontology Alignment}, booktitle = {Proceedings of the 5th International Workshop on Ontology Matching (OM-2010), Shanghai, China, November 7, 2010}, volume = {689}, year = {2010}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Shanghai, China}, abstract = {

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.

}, url = {http://ceur-ws.org/Vol-689/om2010_poster9.pdf}, author = {Prateek Jain and Pascal Hitzler and Amit P. Sheth}, editor = {Pavel Shvaiko and J{\'e}r{\^o}me Euzenat and Fausto Giunchiglia and Heiner Stuckenschmidt and Ming Mao and Isabel F. Cruz} } @article {82, title = {Generalized Distance Functions in the Theory of Computation}, journal = {Computer Journal}, volume = {53}, year = {2010}, pages = {443{\textendash}464}, abstract = {

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.

}, keywords = {denotational semantics, fixed-point theorems, generalized distance functions, Logic Programming, stable model, supported model, topology, ultra-metrics}, doi = {10.1093/comjnl/bxm108}, url = {http://dx.doi.org/10.1093/comjnl/bxm108}, author = {Anthony K. Seda and Pascal Hitzler} } @conference {114, title = {Linked Data Is Merely More Data}, booktitle = {Linked Data Meets Artificial Intelligence, Papers from the 2010 AAAI Spring Symposium}, year = {2010}, publisher = {AAAI}, organization = {AAAI}, address = {Stanford, California, USA}, abstract = {

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 {\textquotedblleft}triple collection,{\textquotedblright} 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.

}, url = {http://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/1130}, author = {Prateek Jain and Pascal Hitzler and Peter Z. Yeh and Kunal Verma and Amit Sheth}, editor = {Dan Brickley and Vinay K. Chaudhri and Harry Halpin and Deborah McGuinness} } @conference {161, title = {A MapReduce Algorithm for EL+}, booktitle = {Proceedings of the 23rd International Workshop on Description Logics (DL 2010)}, volume = {573}, year = {2010}, pages = {464-474}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Waterloo, Ontario, Canada}, abstract = {

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.

}, url = {http://ceur-ws.org/Vol-573/paper_35.pdf}, author = {Raghava Mutharaju and Frederick Maier and Pascal Hitzler}, editor = {Volker Haarslev and David Toman and Grant E. Weddell} } @book {292, title = {Mathematical Aspects of Logic Programming Semantics}, series = {Studies in Informatics}, year = {2010}, pages = {304}, publisher = {Chapman and Hall/CRC Press}, organization = {Chapman and Hall/CRC Press}, issn = {9781439829615}, author = {Pascal Hitzler and Anthony K. Seda} } @conference {26, title = {Normalized MEDLINE Distance in Context-Aware Life Science Literature Searches}, booktitle = {The 4th Chinese Semantic Web Symposium}, year = {2010}, publisher = {Tsinghua Science \& Technology}, organization = {Tsinghua Science \& Technology}, author = {Yan Wang and Cong Wang and Yi Zeng and Zhisheng Huang and Vassil Momtchev and Bo Andersson and Xu Ren and Ning Zhong} } @conference {111, title = {Ontology Alignment for Linked Open Data}, booktitle = {The Semantic Web - ISWC 2010 - 9th International Semantic Web Conference, ISWC 2010}, volume = {6496}, year = {2010}, pages = {402{\textendash}417}, publisher = {Springer}, organization = {Springer}, address = {Shanghai, China}, abstract = {

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.

}, doi = {10.1007/978-3-642-17746-0_26}, url = {http://dx.doi.org/10.1007/978-3-642-17746-0_26}, author = {Prateek Jain and Pascal Hitzler and Amit Sheth and Kunal Verma and Peter Z. Yeh}, editor = {Peter F. Patel-Schneider and Yue Pan and Pascal Hitzler and Peter Mika and Lei Zhang and Jeff Z. Pan and Ian Horrocks and Birte Glimm} } @article {84, title = {Perspectives and challenges for recurrent neural network training}, journal = {Logic Journal of the IGPL}, volume = {18}, year = {2010}, pages = {617{\textendash}619}, doi = {10.1093/jigpal/jzp042}, url = {http://dx.doi.org/10.1093/jigpal/jzp042}, author = {Marco Gori and Barbara Hammer and Pascal Hitzler and Guenther Palm} } @article {85, title = {Preface - Special issue on commonsense reasoning for the semantic web}, journal = {Annals of Mathematics and Artificial Intelligence}, volume = {58}, year = {2010}, pages = {1{\textendash}2}, doi = {10.1007/s10472-010-9209-7}, url = {http://dx.doi.org/10.1007/s10472-010-9209-7}, author = {Frank van Harmelen and Andreas Herzig and Pascal Hitzler and Guilin Qi} } @conference {112, title = {Provenance Context Entity (PaCE): Scalable Provenance Tracking for Scientific RDF Data}, booktitle = {Scientific and Statistical Database Management, 22nd International Conference, SSDBM 2010}, volume = {6187}, year = {2010}, pages = {461{\textendash}470}, publisher = {Springer}, organization = {Springer}, address = {Heidelberg, Germany}, abstract = {

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.\ 

}, keywords = {Biomedical knowledge repository, Context theory, Provenance context entity, Provenance Management Framework., Provenir ontology, RDF reification}, doi = {10.1007/978-3-642-13818-8_32}, url = {http://dx.doi.org/10.1007/978-3-642-13818-8_32}, author = {Satya S. Sahoo and Olivier Bodenreider and Pascal Hitzler and Amit Sheth and Krishnaprasad Thirunarayan}, editor = {Michael Gertz and Bertram Lud{\"a}scher} } @article {78, title = {A Reasonable Semantic Web}, journal = {Semantic Web}, volume = {1}, year = {2010}, pages = {39{\textendash}44}, abstract = {

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 "shared inference," 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.

}, keywords = {Automated Reasoning, Formal Semantics, Knowledge representation, Linked Open Data, Semantic Web}, doi = {10.3233/SW-2010-0010}, url = {http://dx.doi.org/10.3233/SW-2010-0010}, author = {Pascal Hitzler and Frank van Harmelen} } @article {86, title = {Reports of the AAAI 2010 Conference Workshops}, journal = {AI Magazine}, volume = {31}, year = {2010}, pages = {95{\textendash}108}, url = {http://www.aaai.org/ojs/index.php/aimagazine/article/view/2318}, author = {David W. Aha and Mark S. Boddy and Vadim Bulitko and Artur S. d{\textquoteright}Avila Garcez and Prashant Doshi and Stefan Edelkamp and Christopher W. Geib and Piotr J. Gmytrasiewicz and Robert P. Goldman and Pascal Hitzler and Charles L. Isbell and Darsana P. Josyula and Leslie Pack Kaelbling and Kristian Kersting and Maithilee Kunda and Lu{\'\i}s C. Lamb and Bhaskara Marthi and Keith McGreggor and Vivi Nastase and Gregory Provan and Anita Raja and Ashwin Ram and Mark O. Riedl and Stuart J. Russell and Ashish Sabharwal and Jan-Georg Smaus and Gita Sukthankar and Karl Tuyls and Ron van der Meyden and Alon Y. Halevy and Lilyana Mihalkova and Sriraam Natarajan} } @article {79, title = {Semantic Web - Interoperability, Usability, Applicability}, journal = {Semantic Web}, volume = {1}, year = {2010}, pages = {1{\textendash}2}, abstract = {

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.

}, doi = {10.3233/SW-2010-0017}, url = {http://dx.doi.org/10.3233/SW-2010-0017}, author = {Pascal Hitzler and Krzysztof Janowicz} } @proceedings {299, title = {The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part II}, journal = {9th International Semantic Web Conference, ISWC 2010}, volume = {6497}, year = {2010}, publisher = {Springer}, address = {Shanghai, China}, editor = {Peter F. Patel-Schneider and Yue Pan and Pascal Hitzler and Peter Mika and Lei Zhang and Jeff Z. Pan and Ian Horrocks and Birte Glimm} } @proceedings {298, title = {The Semantic Web - ISWC 2010. 9th International Semantic Web Conference, ISWC 2010, Shanghai, China, November 7-11, 2010, Revised Selected Papers, Part I}, journal = {9th International Semantic Web Conference, ISWC 2010}, volume = {6496}, year = {2010}, publisher = {Springer}, address = {Shanghai, China}, editor = {Peter F. Patel-Schneider and Yue Pan and Pascal Hitzler and Peter Mika and Lei Zhang and Jeff Z. Pan and Ian Horrocks and Birte Glimm} } @conference {25, title = {Social Relation Based Search Refinement: Let Your Friends Help You!}, booktitle = {Active Media Technology, 6th International Conference, AMT 2010, Toronto, Canada, August 28-30, 2010. Proceedings}, year = {2010}, pages = {475{\textendash}485}, doi = {10.1007/978-3-642-15470-6_48}, url = {http://dx.doi.org/10.1007/978-3-642-15470-6_48}, author = {Xu Ren and Yi Zeng and Yulin Qin and Ning Zhong and Zhisheng Huang and Yan Wang and Cong Wang} } @conference {105, title = {Targeted Ontology Matching}, booktitle = {International Symposium on Collaborative Technologies and Systems}, year = {2010}, address = {Chicago, IL}, author = {Michelle Cheatham} } @conference {24, title = {User Interests: Definition, Vocabulary, and Utilization in Unifying Search and Reasoning}, booktitle = {Active Media Technology, 6th International Conference, AMT 2010, Toronto, Canada, August 28-30, 2010. Proceedings}, year = {2010}, pages = {98{\textendash}107}, doi = {10.1007/978-3-642-15470-6_11}, url = {http://dx.doi.org/10.1007/978-3-642-15470-6_11}, author = {Yi Zeng and Yan Wang and Zhisheng Huang and Danica Damljanovic and Ning Zhong and Cong Wang} } @proceedings {300, title = {Web Reasoning and Rule Systems. Fourth International Conference, RR 2010, Bressanone, Italy, September 22-24, 2010, Proceedings}, journal = {4th International Conference on Web Reasoning and Rule Systems}, volume = {6333}, year = {2010}, publisher = {Springer}, address = {Bressanone, Italy}, editor = {Pascal Hitzler and Thomas Lukasiewicz} } @conference {116, title = {An Anytime Algorithm for Computing Inconsistency Measurement}, booktitle = {Knowledge Science, Engineering and Management, Third International Conference, KSEM 2009}, volume = {5914}, year = {2009}, pages = {29{\textendash}40}, publisher = {Springer}, organization = {Springer}, address = {Vienna, Austria}, abstract = {

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.

}, doi = {10.1007/978-3-642-10488-6_7}, url = {http://dx.doi.org/10.1007/978-3-642-10488-6_7}, author = {Yue Ma and Guilin Qi and Guohui Xiao and Pascal Hitzler and Zuoquan Lin}, editor = {Dimitris Karagiannis and Zhi Jin} } @proceedings {301, title = {Artificial General Intelligence. Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009. Proceedings}, journal = {Second Conference on Artificial General Intelligence, AGI 2009}, year = {2009}, publisher = {Atlantis Press}, address = {Arlington, Virginia, USA}, issn = {978-90-78677-24-6}, editor = {Ben Goertzel and Pascal Hitzler and Marcus Hutter} } @book {295, title = {Conceptual Structures in Practice}, series = {Studies in Informatics}, year = {2009}, pages = {425}, publisher = {Chapman and Hall/CRC}, organization = {Chapman and Hall/CRC}, issn = {9781420060621}, editor = {Pascal Hitzler and Henrik Sch{\"a}rfe} } @conference {309, title = {An Evolutionary Computing Approach for Reasoning in the Semantic Web}, booktitle = {Poster at DECOI2009, the International Workshop on Collective Intelligence andEvolution}, year = {2009}, address = {Leiden, The Netherlands}, author = {Gaston Tagni and Christophe Gueret and Stefan Schlobach and Sebastian Rudolph and Pascal Hitzler} } @article {87, title = {Facets of Artificial General Intelligence}, journal = {K{\"u}nstliche Intelligenz}, volume = {23}, year = {2009}, pages = {58{\textendash}59}, abstract = {

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.

}, url = {http://www.kuenstliche-intelligenz.de/fileadmin/template/main/archiv/pdf/ki2009-02_page58-59_web_full.pdf}, author = {Pascal Hitzler and Kai-Uwe K{\"u}hnberger} } @book {293, title = {Foundations of Semantic Web Technologies}, series = {Textbooks in Computing}, year = {2009}, pages = {455}, publisher = {Chapman and Hall/CRC Press}, organization = {Chapman and Hall/CRC Press}, issn = {9781420090505}, author = {Pascal Hitzler and Markus Kr{\"o}tzsch and Sebastian Rudolph} } @conference {120, title = {The Importance of Being Neural-Symbolic {\textendash} A Wilde Position}, booktitle = {Second Conference on Artificial General Intelligence, AGI 2009}, year = {2009}, pages = {208-209}, address = {Arlington, Virginia, USA}, abstract = {

We argue that Neural-Symbolic Integration is a topic of central importance for the advancement of Artificial General Intelligence.

}, author = {Pascal Hitzler and Kai-Uwe K{\"u}hnberger}, editor = {Ben Goertzel and Pascal Hitzler and Marcus Hutter} } @article {88, title = {KI 2009 - AI Mashup Challenge 2009}, journal = {KI - K{\"u}nstliche Intelligenz}, year = {2009}, pages = {52}, author = {Brigitte Endres-Niggemeyer and Pascal Hitzler and Valentin Zacharias} } @inbook {92, title = {Ontologies and Rules}, booktitle = {Handbook on Ontologies}, year = {2009}, pages = {111-132}, publisher = {Springer}, organization = {Springer}, edition = {2}, author = {Pascal Hitzler and Bijan Parsia}, editor = {Steffen Staab and Rudi Studer} } @conference {168, title = {Ontology Driven Integration of Biology Experiment Data}, booktitle = {Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters \& Demos}, year = {2009}, address = {Cleveland, OH, USA}, author = {Raghava Mutharaju and Satya S. Sahoo and D. Brent Weatherly and Pramod Anantharam and Flora Logan and Amit Sheth and Rick Tarleton} } @conference {162, title = {Ontology-Driven Provenance Management in eScience: An Application in Parasite Research}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2009, Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009, Proceedings, Part II}, volume = {5871}, year = {2009}, pages = {992{\textendash}1009}, publisher = {Springer}, organization = {Springer}, address = {Vilamoura, Portugal}, abstract = {

Provenance, from the French word {\textquotedblleft}provenir{\textquotedblright}, 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.

}, doi = {10.1007/978-3-642-05151-7_18}, url = {http://dx.doi.org/10.1007/978-3-642-05151-7_18}, author = {Satya S. Sahoo and D. Brent Weatherly and Raghava Mutharaju and Pramod Anantharam and Amit Sheth and Rick Tarleton}, editor = {Robert Meersman and Tharam S. Dillon and Pilar Herrero} } @article {304, title = {OWL 2 Web Ontology Language: Primer}, year = {2009}, month = {10/27/2009}, pages = {W3C Recommendation}, url = {http://www.w3.org/TR/2009/REC-owl2-primer-20091027}, author = {Pascal Hitzler and Markus Kr{\"o}tzsch and Bijan Parsia and Peter F. Patel-Schneider and Sebastian Rudolph} } @conference {118, title = {Paraconsistent Reasoning for OWL 2}, booktitle = {Web Reasoning and Rule Systems, Third International Conference, RR 2009}, volume = {5837}, year = {2009}, pages = {197{\textendash}211}, publisher = {Springer}, organization = {Springer}, address = {Chantilly, VA, USA}, abstract = {

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.

}, doi = {10.1007/978-3-642-05082-4_14}, url = {http://dx.doi.org/10.1007/978-3-642-05082-4_14}, author = {Yue Ma and Pascal Hitzler}, editor = {Axel Polleres and Terrance Swift} } @conference {117, title = {A Preferential Tableaux Calculus for Circumscriptive ALCO}, booktitle = {Web Reasoning and Rule Systems, Third International Conference, RR 2009}, volume = {5837}, year = {2009}, pages = {40{\textendash}54}, publisher = {Springer}, organization = {Springer}, address = {Chantilly, VA, USA}, abstract = {

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{\textquoteright}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.

}, doi = {10.1007/978-3-642-05082-4_4}, url = {http://dx.doi.org/10.1007/978-3-642-05082-4_4}, author = {Stephan Grimm and Pascal Hitzler}, editor = {Axel Polleres and Terrance Swift} } @proceedings {316, title = {Proceedings of the Fifth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy{\textquoteright}09, at the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, July 2009}, journal = {Fifth International Workshop on Neural-Symbolic Learning and Reasoning, NeSy{\textquoteright}09, at the 21st International Joint Conference on Artificial Intelligence}, volume = {481}, year = {2009}, publisher = {CEUR-WS.org}, address = {Pasadena, California}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-481}, editor = {Artur S. d{\textquoteright}Avila Garcez and Pascal Hitzler} } @proceedings {315, title = {Proceedings of the Fourth International Workshop on Applications of Semantic Technologies, AST2009, at Informatik2009, L{\"u}beck, Germany, October 2009}, journal = {INFORMATIK 2009 - Im Fokus das Leben}, year = {2009}, pages = {381-400}, publisher = {Bonner K{\"o}llen Verlag}, address = {L{\"u}beck, Germany}, issn = {978-3-88579-248-2}, editor = {Stephan Grimm and Pascal Hitzler} } @conference {119, title = {RaDON - Repair and Diagnosis in Ontology Networks}, booktitle = {The Semantic Web: Research and Applications, 6th European Semantic Web Conference, ESWC 2009}, volume = {5554}, year = {2009}, pages = {863{\textendash}867}, publisher = {Springer}, organization = {Springer}, address = {Heraklion, Crete, Greece}, abstract = {

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.

}, doi = {10.1007/978-3-642-02121-3_71}, url = {http://dx.doi.org/10.1007/978-3-642-02121-3_71}, author = {Qiu Ji and Peter Haase and Guilin Qi and Pascal Hitzler and Steffen Stadtm{\"u}ller}, editor = {Lora Aroyo and Paolo Traverso and Fabio Ciravegna and Philipp Cimiano and Tom Heath and Eero Hyv{\"o}nen and Riichiro Mizoguchi and Eyal Oren and Marta Sabou and Elena Paslaru Bontas Simperl} } @conference {163, title = {Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences}, booktitle = {Web Information Systems Engineering - WISE 2009, 10th International Conference}, volume = {5802}, year = {2009}, pages = {539{\textendash}553}, publisher = {Springer}, organization = {Springer}, address = {Poznan, Poland}, abstract = {

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.

}, doi = {10.1007/978-3-642-04409-0_52}, url = {http://dx.doi.org/10.1007/978-3-642-04409-0_52}, author = {Meenakshi Nagarajan and Karthik Gomadam and Amit Sheth and Ajith Ranabahu and Raghava Mutharaju and Ashutosh Jadhav}, editor = {Gottfried Vossen and Darrell D. E. Long and Jeffrey Xu Yu} } @conference {406, title = {Suggestions for OWL 3}, booktitle = {Proceedings of the 5th International Workshop on {OWL:} Experiences and Directions {(OWLED} 2009), Chantilly, VA, United States, October 23-24, 2009}, volume = {529}, year = {2009}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Chantilly, VA, United States}, abstract = {

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.

}, url = {http://ceur-ws.org/Vol-529/owled2009_submission_6.pdf}, author = {Pascal Hitzler}, editor = {Rinke Hoekstra and Peter F. Patel-Schneider} } @conference {115, title = {Towards Reasoning Pragmatics}, booktitle = {GeoSpatial Semantics, Third International Conference, GeoS 2009}, volume = {5892}, year = {2009}, pages = {9{\textendash}25}, publisher = {Springer}, organization = {Springer}, address = {Mexico City, Mexico}, abstract = {

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.

}, doi = {10.1007/978-3-642-10436-7_2}, url = {http://dx.doi.org/10.1007/978-3-642-10436-7_2}, author = {Pascal Hitzler}, editor = {Krzysztof Janowicz and Martin Raubal and Sergei Levashkin} } @conference {169, title = {Trykipedia: Collaborative Bio-Ontology Development using Wiki Environment}, booktitle = {Ohio Collaborative Conference on BioInformatics (OCCBIO 2009), Posters \& Demos}, year = {2009}, address = {Cleveland, OH, USA}, author = {Pramod Anantharam and Satya S. Sahoo and D. Brent Weatherly and Flora Logan and Raghava Mutharaju and Amit Sheth and Rick Tarleton} } @conference {165, title = {Twitris: Socially Influenced Browsing}, booktitle = {Semantic Web Challenge at the 8th International Semantic Web Conference (ISWC 2009)}, year = {2009}, address = {Washington DC, USA}, abstract = {

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.

}, author = {Ashutosh Jadhav and Wenbo Wang and Raghava Mutharaju and Pramod Anantharam and Vinh Nguyen and Amit Sheth and Karthik Gomadam and Meenakshi Nagarajan and Ajith Ranabahu} } @book {294, title = {Semantic Web Grundlagen}, year = {2008}, pages = {277}, publisher = {Springer textbook}, organization = {Springer textbook}, issn = {978-3-540-33993-9}, author = {Pascal Hitzler and Markus Kr{\"o}tzsch and Sebastian Rudolph and York Sure} } @conference {137, title = {Data Complexity in the {EL} Family of Description Logics}, booktitle = {Logic for Programming, Artificial Intelligence, and Reasoning, 14th International Conference, LPAR 2007, Yerevan, Armenia, October 15-19, 2007, Proceedings}, volume = {4790}, year = {2007}, month = {10/2007}, pages = {333-347}, publisher = {Springer}, organization = {Springer}, abstract = {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}, doi = {10.1007/978-3-540-75560-9_25}, url = {http://dx.doi.org/10.1007/978-3-540-75560-9_25}, author = {Adila Krisnadhi and Carsten Lutz}, editor = {Nachum Dershowitz and Andrei Voronkov} } @conference {138, title = {Data Complexity in the {EL} family of DLs}, booktitle = {Proceedings of the 2007 International Workshop on Description Logics (DL2007), Brixen-Bressanone, near Bozen-Bolzano, Italy, 8-10 June, 2007}, volume = {250}, year = {2007}, month = {06/2007}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, url = {http://ceur-ws.org/Vol-250/paper_15.pdf}, author = {Adila Krisnadhi and Carsten Lutz}, editor = {Diego Calvanese and Enrico Franconi and Volker Haarslev and Domenico Lembo and Boris Motik and Anni-Yasmin Turhan and Sergio Tessaris} } @mastersthesis {136, title = {Data Complexity of Instance Checking in the {EL} Family of Description Logics}, volume = {Master of Science}, year = {2007}, month = {03/2007}, pages = {v+68}, school = {Technische Universit{\"a}t Dresden}, type = {Master{\textquoteright}s}, address = {Dresden}, abstract = {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.}, url = {http://lat.inf.tu-dresden.de/research/mas/$\#$Kri-Mas-07}, author = {Adila Krisnadhi} } @conference {106, title = {Feature Selection for Collaborative Team Formation via Social Network Analysis}, booktitle = {International Conference on Data Mining}, year = {2007}, address = {Las Vegas, NV}, author = {Michelle Cheatham and Felicia Harlow and Kevin Cleereman} } @conference {107, title = {Application of Social Network Analysis to Collaborative Team Formation}, booktitle = {International Symposium on Collaborative Technologies and Systems}, year = {2006}, address = {Las Vegas, NV}, author = {Michelle Cheatham and Kevin Cleereman} } @conference {108, title = {Feature and Prototype Evolution for Nearest Neighbor Classification of Web Documents}, booktitle = {International Conference on Information Technology - New Generations}, year = {2006}, address = {Las Vegas, NV}, author = {Michelle Cheatham and Mateen Rizki} } @article {171, title = {Traceability from Use Case to .NET Assembly via Design Patterns}, year = {2006}, institution = {Motilal Nehru National Institute of Technology (MNNIT)}, address = {Allahabad, India}, author = {Raghava Mutharaju and Banshi D. Chaudhary} } @conference {110, title = {AI Planning in Portal-based Workflow Management Systems}, booktitle = {International Conference on Integration of Knowledge Intensive Multi-Agent Systems}, year = {2005}, address = {Waltham, MA}, author = {Michelle Cheatham and Michael Cox} } @conference {109, title = {AI Workflow Management in a Collaborative Environment}, booktitle = {International Symposium on Collaborative Technologies and Systems}, year = {2005}, address = {St. Louis, MO}, author = {Michelle Cheatham and Michael Cox} } @article {967, title = {InK Browser {\textendash} The Interactive Knowledge Browser}, pages = {Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 20th In}, author = {Joseph Zalweski and Lu Zhou and Cogan Shimizu and Pascal Hitzler} } @article {966, title = {Knowledge Explorer: Exploring the 12-Billion-Statement KnowWhereGraph Using Faceted Search (demo paper)}, pages = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2022}, address = {Seattle, Washington}, author = {Zilong Liu and Zhining Gu and Thomas Thelen and Seila Estrecha and Rui Zhu and Colby Fisher and Anthony D{\textquoteright}Onofrio and Cogan Shimizu and Krzysztof Janowicz and Mark Schildhauer and Shirly Stephen and Dean Rehberger and Wenwen Li and Pascal Hitzler} }