<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Samatha Ereshi Akkamahadevi</style></author><author><style face="normal" font="default" size="100%">Abhilekha Dalal</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automating CNN Neuron Interpretation using Concept Induction</style></title><secondary-title><style face="normal" font="default" size="100%">THE 23RD INTERNATIONAL SEMANTIC WEB CONFERENCE, ISWC 2024</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automation in AI</style></keyword><keyword><style  face="normal" font="default" size="100%">Deep Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Explainable Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge Graph</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper presents an automation pipeline for interpreting hidden neuron activations in Convolutional Neural Networks (CNNs), a crucial objective of Explainable AI (XAI). Previously, our research group addressed this objective by employing concept induction and semantic reasoning using a concept hierarchy derived from the Wikipedia knowledge graph. However, the process was executed manually, taking several days to complete. In this study, we have fully automated the workflow, achieving consistent results while significantly reducing the execution time. The automation pipeline streamlines model training, data preparation, concept induction, image retrieval, classification, and statistical validation, thereby completely eliminating the manual intervention. This automation enables us to efficiently interpret and validate CNN neuron activations by modifying parameters, such as incorporating a broader range of training images and classes and examining additional concept induction results across various neuron layers using different analytical tools.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cogan Shimizu</style></author><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Nazifa Karima</style></author><author><style face="normal" font="default" size="100%">Quinn Hirt</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Method for Automatically Generating Schema Diagrams for OWL Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">1st Iberoamerican Knowledge Graph and Semantic Web Conference (KGSWC)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">design patterns</style></keyword><keyword><style  face="normal" font="default" size="100%">evaluation</style></keyword><keyword><style  face="normal" font="default" size="100%">implementation</style></keyword><keyword><style  face="normal" font="default" size="100%">ontology</style></keyword><keyword><style  face="normal" font="default" size="100%">schema diagrams</style></keyword><keyword><style  face="normal" font="default" size="100%">visualization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2019</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Villa Clara, Cuba</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Interest in Semantic Web technologies, including knowledge graphs and ontologies, is increasing rapidly in industry and academics. In order to support ontology engineers and domain experts, it is necessary to provide them with robust tools that facilitate the ontology engineering process. Often, the schema diagram of an ontology is the most important tool for quickly conveying the overall purpose of an ontology. In this paper, we present a method for programmatically generating a schema diagram from an OWL file. We evaluate its ability to generate schema diagrams similar to manually drawn schema diagrams and show that it outperforms VOWL and OWLGrEd. In addition, we provide a prototype implementation of this tool.&lt;/p&gt;
</style></abstract><section><style face="normal" font="default" size="100%">149-161</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aaron Eberhart</style></author><author><style face="normal" font="default" size="100%">Michelle Cheatham</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pseudo-Random ALC Syntax Generation</style></title><secondary-title><style face="normal" font="default" size="100%">The Semantic Web: ESWC 2018 Satellite Events - ESWC 2018 Satellite Events, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ALC</style></keyword><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">DL</style></keyword><keyword><style  face="normal" font="default" size="100%">random generation</style></keyword><keyword><style  face="normal" font="default" size="100%">synthetic data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/978-3-319-98192-5\_4</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heraklion, Crete, Greece</style></pub-location><volume><style face="normal" font="default" size="100%">11155</style></volume><pages><style face="normal" font="default" size="100%">19–22</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-98191-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We discuss a tool capable of rapidly generating pseudo-random syntactically valid ALC expression trees. The program is meant to allow a researcher to create large sets of independently valid expressions with a minimum of personal bias for experimentation.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Prabhaker Mateti</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Thorsten Liebig</style></author><author><style face="normal" font="default" size="100%">Achille Fokoue</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Distributed OWL EL Reasoning: The Story So Far</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Distributed Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL EL</style></keyword><keyword><style  face="normal" font="default" size="100%">Scalability</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Riva del Garda, Italy</style></pub-location><volume><style face="normal" font="default" size="100%">1261</style></volume><pages><style face="normal" font="default" size="100%">61-76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires distributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now exist. For OWL 2 EL, several distributed reasoning approaches have been tried, but are all perceived to be inefficient. We analyze this perception. We analyze completion rule based distributed approaches, using different characteristics, such as dependency among the rules, implementation optimizations, how axioms and rules are distributed. We also present a distributed queue approach for the classification of ontologies in description logic EL+ (fragment of OWL 2 EL).&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Prabhaker Mateti</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Thorsten Liebig</style></author><author><style face="normal" font="default" size="100%">Achille Fokoue</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">DistEL: A Distributed EL+ Ontology Classifier</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, co-located with the International Semantic Web Conference (ISWC 2013)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Classification</style></keyword><keyword><style  face="normal" font="default" size="100%">DistEL</style></keyword><keyword><style  face="normal" font="default" size="100%">Distributed Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">EL+</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Scalability</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2013</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Sydney, Australia</style></pub-location><volume><style face="normal" font="default" size="100%">1046</style></volume><pages><style face="normal" font="default" size="100%">17-32</style></pages><abstract><style face="normal" font="default" size="100%">OWL 2 EL ontologies are used to model and reason over data from diverse domains such as biomedicine, geography and road traffic. Data in these domains is increasing at a rate quicker than the increase in main memory and computation power of a single machine. Recent efforts in OWL reasoning algorithms lead to the decrease in classification time from several hours to a few seconds even for large ontologies like SNOMED CT. This is especially true for ontologies in the description logic EL+ (a fragment of the OWL 2 EL profile). Reasoners such as Pellet, Hermit, ELK etc. make an assumption that the ontology would fit in the main memory, which is unreasonable given projected increase in data volumes. Increase in the data volume also necessitates an increase in the computation power. This lead us to the use of a distributed system, so that memory and computation requirements can be spread across machines. We present a distributed system for the classification of EL+ ontologies along with some results on its scalability and performance.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Raghava Mutharaju</style></author><author><style face="normal" font="default" size="100%">Sherif Sakr</style></author><author><style face="normal" font="default" size="100%">Alessandra Sala</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Eva Blomqvist</style></author><author><style face="normal" font="default" size="100%">Tudor Groza</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">D-SPARQ: Distributed, Scalable and Efficient RDF Query Engine</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ISWC 2013 Posters &amp; Demonstrations Track</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">D-SPARQ</style></keyword><keyword><style  face="normal" font="default" size="100%">Distributed Querying</style></keyword><keyword><style  face="normal" font="default" size="100%">Scalable RDF querying</style></keyword><keyword><style  face="normal" font="default" size="100%">SPARQL</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1035/iswc2013_poster_21.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CEUR-WS.org</style></publisher><pub-location><style face="normal" font="default" size="100%">Sydney, Australia</style></pub-location><volume><style face="normal" font="default" size="100%">1035</style></volume><pages><style face="normal" font="default" size="100%">261–264</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We present D-SPARQ, a distributed RDF query engine that combines the MapReduce processing framework with a NoSQL distributed data store, MongoDB. The performance of processing SPARQL queries mainly depends on the efficiency of handling the join operations between the RDF triple patterns. Our system features two unique characteristics that enable efficiently tackling this challenge: 1) Identifying specific patterns of the input queries that enable improving the performance by running different parts of the query in a parallel mode. 2) Using the triple selectivity information for reordering the individual triples of the input query within the identified query patterns. The preliminary results demonstrate the scalability and efficiency of our distributed RDF query engine.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frederick Maier</style></author><author><style face="normal" font="default" size="100%">Yue Ma</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Paraconsistent OWL and Related Logics</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automated Deduction</style></keyword><keyword><style  face="normal" font="default" size="100%">Complexity</style></keyword><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">Paraconsistency</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword><keyword><style  face="normal" font="default" size="100%">Web Ontology Language</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2012-0066</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">395–427</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Web Ontology Language OWL is currently the most prominent formalism for representing ontologies in Semantic Web applications. OWL is based on description logics, and automated reasoners are used to infer knowledge implicitly present in OWL ontologies. However, because typical description logics obey the classical principle of explosion, reasoning over inconsistent ontologies is impossible in OWL. This is so despite the fact that inconsistencies are bound to occur in many realistic cases, e.g., when multiple ontologies are merged or when ontologies are created by machine learning or data mining tools. In this paper, we present four-valued paraconsistent description logics which can reason over inconsistencies. We focus on logics corresponding to OWL DL and its profiles. We present the logic SROIQ4, showing that it is both sound relative to classical SROIQ and that its embedding into SROIQ is consequence preserving. We also examine paraconsistent varieties of EL++, DL-Lite, and Horn-DLs. The general framework described here has the distinct advantage of allowing classical reasoners to draw sound but nontrivial conclusions from even inconsistent knowledge bases. Truth-value gaps and gluts can also be selectively eliminated from models (by inserting additional axioms into knowledge bases). If gaps but not gluts are eliminated, additional classical conclusions can be drawn without affecting paraconsistency.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Krötzsch</style></author><author><style face="normal" font="default" size="100%">Frederick Maier</style></author><author><style face="normal" font="default" size="100%">Adila Krisnadhi</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Sadagopan Srinivasan</style></author><author><style face="normal" font="default" size="100%">Krithi Ramamritham</style></author><author><style face="normal" font="default" size="100%">Arun Kumar</style></author><author><style face="normal" font="default" size="100%">M. P. Ravindra</style></author><author><style face="normal" font="default" size="100%">Elisa Bertino</style></author><author><style face="normal" font="default" size="100%">Ravi Kumar</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Better Uncle for OWL: Nominal Schemas for Integrating Rules and Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 20th International Conference on World Wide Web, WWW 2011, Hyderabad, India, March 28 - April 1, 2011</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">datalog</style></keyword><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web Rule Language</style></keyword><keyword><style  face="normal" font="default" size="100%">SROIQ</style></keyword><keyword><style  face="normal" font="default" size="100%">tractability</style></keyword><keyword><style  face="normal" font="default" size="100%">Web Ontology Language</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/1963405.1963496</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">645-654</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4503-0632-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a description-logic style extension of OWL 2 with nominal schemas which can be used like &quot;variable nominal classes&quot; within axioms. This feature allows ontology languages to express arbitrary DL-safe rules (as expressible in SWRL or RIF) in their native syntax. We show that adding nominal schemas to OWL 2 does not increase the worst-case reasoning complexity, and we identify a novel tractable language SROELV3(\cap, x) that is versatile enough to capture the lightweight languages OWL EL and OWL RL.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Matthias Knorr</style></author><author><style face="normal" font="default" size="100%">José Júlio Alferes</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Local Closed World Reasoning with Description Logics under the Well-Founded Semantics</style></title><secondary-title><style face="normal" font="default" size="100%">Artificial Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Description Logic</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Logic Programming</style></keyword><keyword><style  face="normal" font="default" size="100%">Non-monotonic reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Ontologies</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1016/j.artint.2011.01.007</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">175</style></volume><pages><style face="normal" font="default" size="100%">1528–1554</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases [52], and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9-10</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Barbara Hammer</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Wolfgang Maass</style></author><author><style face="normal" font="default" size="100%">Marc Toussaint</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">10302 Abstracts Collection - Learning paradigms in dynamic environments</style></title><secondary-title><style face="normal" font="default" size="100%">Learning paradigms in dynamic environments</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autonomous learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural-symbolic integration</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurobiology</style></keyword><keyword><style  face="normal" font="default" size="100%">Recurrent neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Speech processing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://drops.dagstuhl.de/opus/volltexte/2010/2804</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany</style></publisher><pub-location><style face="normal" font="default" size="100%">Dagstuhl, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jens Lehmann</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Concept learning in description logics using refinement operators</style></title><secondary-title><style face="normal" font="default" size="100%">Machine Learning</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">description logics</style></keyword><keyword><style  face="normal" font="default" size="100%">Inductive logic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">OWL</style></keyword><keyword><style  face="normal" font="default" size="100%">refinement operators</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword><keyword><style  face="normal" font="default" size="100%">Structured Machine Learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://springerlink.metapress.com/content/c040n45u15qrnu44/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">203–250</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anthony K. Seda</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Generalized Distance Functions in the Theory of Computation</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">denotational semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">fixed-point theorems</style></keyword><keyword><style  face="normal" font="default" size="100%">generalized distance functions</style></keyword><keyword><style  face="normal" font="default" size="100%">Logic Programming</style></keyword><keyword><style  face="normal" font="default" size="100%">stable model</style></keyword><keyword><style  face="normal" font="default" size="100%">supported model</style></keyword><keyword><style  face="normal" font="default" size="100%">topology</style></keyword><keyword><style  face="normal" font="default" size="100%">ultra-metrics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1093/comjnl/bxm108</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">53</style></volume><pages><style face="normal" font="default" size="100%">443–464</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We discuss a number of distance functions encountered in the theory of computation, including metrics, ultra-metrics, quasi-metrics, generalized ultra-metrics, partial metrics, d-ultra-metrics and generalized metrics. We consider their properties, associated fixed-point theorems and some general applications they have within the theory of computation. We consider in detail the applications of generalized distance functions in giving a uniform treatment of several important semantics for logic programs, including acceptable programs and natural generalizations of them, and also the supported model and the stable model in the context of locally stratified extended disjunctive logic programs and databases.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Reasonable Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automated Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Formal Semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Linked Open Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2010-0010</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">39–44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as &quot;shared inference,&quot; which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on entirely different methods like machine learning or natureinspired computing need to be investigated, while quality assurance needs to be done in terms of precision and recall values (as in information retrieval) and not necessarily in terms of soundness and completeness of the underlying algorithms.&lt;/p&gt;
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