PUMA publications for /tag/ontologyhttps://puma.uni-kassel.de/tag/ontologyPUMA RSS feed for /tag/ontology2024-03-19T03:19:08+01:00An Introduction to Ontology Learninghttps://puma.uni-kassel.de/bibtex/2cf6a6785f5cab0525632a003c47ef5f7/hothohotho2015-01-29T15:09:38+01:00introduction learning ontology <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jens Lehmann" itemprop="url" href="/author/Jens%20Lehmann"><span itemprop="name">J. Lehmann</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Johanna Voelker" itemprop="url" href="/author/Johanna%20Voelker"><span itemprop="name">J. Voelker</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Perspectives on Ontology Learning</span>, </em><em><span itemprop="publisher">AKA / IOS Press</span>, </em></span>(<em><span>2014<meta content="2014" itemprop="datePublished"/></span></em>)Thu Jan 29 15:09:38 CET 2015Perspectives on Ontology Learningix-xviAn Introduction to Ontology Learning2014introduction learning ontology Never-Ending Learninghttps://puma.uni-kassel.de/bibtex/263070703e6bb812852cca56574aed093/hothohotho2015-01-27T15:35:24+01:00learning nell ontology semantic toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. Mitchell" itemprop="url" href="/author/T.%20Mitchell"><span itemprop="name">T. Mitchell</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="W. Cohen" itemprop="url" href="/author/W.%20Cohen"><span itemprop="name">W. Cohen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="E. Hruscha" itemprop="url" href="/author/E.%20Hruscha"><span itemprop="name">E. Hruscha</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. Talukdar" itemprop="url" href="/author/P.%20Talukdar"><span itemprop="name">P. Talukdar</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. Betteridge" itemprop="url" href="/author/J.%20Betteridge"><span itemprop="name">J. Betteridge</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Carlson" itemprop="url" href="/author/A.%20Carlson"><span itemprop="name">A. Carlson</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="B. Dalvi" itemprop="url" href="/author/B.%20Dalvi"><span itemprop="name">B. Dalvi</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Gardner" itemprop="url" href="/author/M.%20Gardner"><span itemprop="name">M. Gardner</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="B. Kisiel" itemprop="url" href="/author/B.%20Kisiel"><span itemprop="name">B. Kisiel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. Krishnamurthy" itemprop="url" href="/author/J.%20Krishnamurthy"><span itemprop="name">J. Krishnamurthy</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="N. Lao" itemprop="url" href="/author/N.%20Lao"><span itemprop="name">N. Lao</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="K. Mazaitis" itemprop="url" href="/author/K.%20Mazaitis"><span itemprop="name">K. Mazaitis</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. Mohammad" itemprop="url" href="/author/T.%20Mohammad"><span itemprop="name">T. Mohammad</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="N. Nakashole" itemprop="url" href="/author/N.%20Nakashole"><span itemprop="name">N. Nakashole</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="E. Platanios" itemprop="url" href="/author/E.%20Platanios"><span itemprop="name">E. Platanios</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Ritter" itemprop="url" href="/author/A.%20Ritter"><span itemprop="name">A. Ritter</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Samadi" itemprop="url" href="/author/M.%20Samadi"><span itemprop="name">M. Samadi</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="B. Settles" itemprop="url" href="/author/B.%20Settles"><span itemprop="name">B. Settles</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Wang" itemprop="url" href="/author/R.%20Wang"><span itemprop="name">R. Wang</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="D. Wijaya" itemprop="url" href="/author/D.%20Wijaya"><span itemprop="name">D. Wijaya</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Gupta" itemprop="url" href="/author/A.%20Gupta"><span itemprop="name">A. Gupta</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="X. Chen" itemprop="url" href="/author/X.%20Chen"><span itemprop="name">X. Chen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Saparov" itemprop="url" href="/author/A.%20Saparov"><span itemprop="name">A. Saparov</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Greaves" itemprop="url" href="/author/M.%20Greaves"><span itemprop="name">M. Greaves</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="J. Welling" itemprop="url" href="/author/J.%20Welling"><span itemprop="name">J. Welling</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">AAAI</span>, </em></span>(<em><span>2015<meta content="2015" itemprop="datePublished"/></span></em>)<em>: Never-Ending Learning in AAAI-2015.</em>Tue Jan 27 15:35:24 CET 2015AAAI: Never-Ending Learning in AAAI-2015Never-Ending Learning2015learning nell ontology semantic toread Papers by William W. CohenHandbook on ontologieshttps://puma.uni-kassel.de/bibtex/2be122d99dc6dd20cb58a55d62d8eca6c/hothohotho2014-11-20T09:22:47+01:00handbook ontology sota survey <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Staab" itemprop="url" href="/author/Steffen%20Staab"><span itemprop="name">S. Staab</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Rudi Studer" itemprop="url" href="/author/Rudi%20Studer"><span itemprop="name">R. Studer</span></a></span>. </span><em><span itemprop="publisher">Springer</span>, </em><em>Berlin, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Thu Nov 20 09:22:47 CET 2014BerlinHandbook on ontologies2009handbook ontology sota survey An ontology is a formal description of concepts and relationships that can exist for a community of human and/or machine agents. This book considers ontology languages, ontology engineering methods, example ontologies, infrastructures and technologies for ontologies, and how to bring this all into ontology-based infrastructures and applications.Handbook on OntologiesOntology-Driven Software Developmenthttps://puma.uni-kassel.de/bibtex/2b88adb2114769033172f3974ad1aaaac/illigillig2014-07-15T22:27:09+02:00ontology driven software engineering book <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="editor"><a title="Jeff Z. Pan" itemprop="url" href="/author/Jeff%20Z.%20Pan"><span itemprop="name">J. Pan</span></a></span> (Hrsg.).
. </span><em><span itemprop="publisher">Springer</span>, </em><em>Berlin u.a., </em>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Tue Jul 15 22:27:09 CEST 2014Berlin [u.a.]Ontology-Driven Software Development2013ontology driven software engineering book YAGO: a core of semantic knowledgehttps://puma.uni-kassel.de/bibtex/284ae693c0a6dfb6d4b051b0b6dbd3668/jaeschkejaeschke2012-12-18T20:33:37+01:00data knowledge linked lod ontology open semantic web yago <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Fabian M. Suchanek" itemprop="url" href="/author/Fabian%20M.%20Suchanek"><span itemprop="name">F. Suchanek</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gjergji Kasneci" itemprop="url" href="/author/Gjergji%20Kasneci"><span itemprop="name">G. Kasneci</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerhard Weikum" itemprop="url" href="/author/Gerhard%20Weikum"><span itemprop="name">G. Weikum</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 16th international conference on World Wide Web</span>, </em></span><em>Seite <span itemprop="pagination">697--706</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Tue Dec 18 20:33:37 CET 2012New York, NY, USAProceedings of the 16th international conference on World Wide Web697--706YAGO: a core of semantic knowledge2007data knowledge linked lod ontology open semantic web yago We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in <i>quality</i> by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in <i>quantity</i> by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.Completing description logic knowledge bases using formal concept analysishttps://puma.uni-kassel.de/bibtex/287f98ae486014ba78690ffa314b67da8/jaeschkejaeschke2012-11-18T14:23:57+01:00analysis base complete concept description dl fca formal knowledge logic ontology <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Franz Baader" itemprop="url" href="/author/Franz%20Baader"><span itemprop="name">F. Baader</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bernhard Ganter" itemprop="url" href="/author/Bernhard%20Ganter"><span itemprop="name">B. Ganter</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Baris Sertkaya" itemprop="url" href="/author/Baris%20Sertkaya"><span itemprop="name">B. Sertkaya</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ulrike Sattler" itemprop="url" href="/author/Ulrike%20Sattler"><span itemprop="name">U. Sattler</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 20th international joint conference on Artifical intelligence</span>, </em></span><em>Seite <span itemprop="pagination">230--235</span>. </em><em>San Francisco, CA, USA, </em><em><span itemprop="publisher">Morgan Kaufmann Publishers Inc.</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Sun Nov 18 14:23:57 CET 2012San Francisco, CA, USAProceedings of the 20th international joint conference on Artifical intelligence230--235Completing description logic knowledge bases using formal concept analysis2007analysis base complete concept description dl fca formal knowledge logic ontology We propose an approach for extending both the terminological and the assertional part of a Description Logic knowledge base by using information provided by the knowledge base and by a domain expert. The use of techniques from Formal Concept Analysis ensures that, on the one hand, the interaction with the expert is kept to a minimum, and, on the other hand, we can show that the extended knowledge base is complete in a certain, well-defined sense.Automatic acquisition of hyponyms from large text corporahttps://puma.uni-kassel.de/bibtex/22c49ad19ac6977bd806b6687e4dcc550/jaeschkejaeschke2012-11-16T14:08:07+01:00corpus hearst learning linguistics ontology pattern text <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marti A. Hearst" itemprop="url" href="/author/Marti%20A.%20Hearst"><span itemprop="name">M. Hearst</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 14th conference on Computational linguistics</span>, </em></span><em> 2, </em><em>Seite <span itemprop="pagination">539--545</span>. </em><em>Stroudsburg, PA, USA, </em><em><span itemprop="publisher">Association for Computational Linguistics</span>, </em>(<em><span>1992<meta content="1992" itemprop="datePublished"/></span></em>)Fri Nov 16 14:08:07 CET 2012Stroudsburg, PA, USAProceedings of the 14th conference on Computational linguistics539--545Automatic acquisition of hyponyms from large text corpora21992corpus hearst learning linguistics ontology pattern text We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidance of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexico-syntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also be acquirable in this way. A subset of the acquisition algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.Ontology Evolution: Not the Same as Schema Evolutionhttps://puma.uni-kassel.de/bibtex/208ee0381e240c3ee414e0eefc7fe1a83/jaeschkejaeschke2012-09-05T11:09:29+02:00database evolution ontology schema semantic web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Natalya F. Noy" itemprop="url" href="/author/Natalya%20F.%20Noy"><span itemprop="name">N. Noy</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Michel Klein" itemprop="url" href="/author/Michel%20Klein"><span itemprop="name">M. Klein</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Knowledge and Information Systems</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">6 </span></span>(<span itemprop="issueNumber">4</span>):
<span itemprop="pagination">428--440</span></em> </span>(<em><span>2004<meta content="2004" itemprop="datePublished"/></span></em>)Wed Sep 05 11:09:29 CEST 2012LondonKnowledge and Information Systems4428--440Ontology Evolution: Not the Same as Schema Evolution62004database evolution ontology schema semantic web As ontology development becomes a more ubiquitous and collaborative process, ontology versioning and evolution becomes an important area of ontology research. The many similarities between database-schema evolution and ontology evolution will allow us to build on the extensive research in schema evolution. However, there are also important differences between database schemas and ontologies. The differences stem from different usage paradigms, the presence of explicit semantics and different knowledge models. A lot of problems that existed only in theory in database research come to the forefront as practical problems in ontology evolution. These differences have important implications for the development of ontology-evolution frameworks: The traditional distinction between versioning and evolution is not applicable to ontologies. There are several dimensions along which compatibility between versions must be considered. The set of change operations for ontologies is different. We must develop automatic techniques for finding similarities and differences between versions.Extracting relevant questions to an RDF dataset using formal concept analysishttps://puma.uni-kassel.de/bibtex/245374b975834248c0cd87022fc854e25/jaeschkejaeschke2012-01-20T10:30:56+01:00analysis concept fca formal ontology rdf semantic web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mathieu d'Aquin" itemprop="url" href="/author/Mathieu%20d'Aquin"><span itemprop="name">M. d'Aquin</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Enrico Motta" itemprop="url" href="/author/Enrico%20Motta"><span itemprop="name">E. Motta</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the sixth international conference on Knowledge capture</span>, </em></span><em>Seite <span itemprop="pagination">121--128</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Fri Jan 20 10:30:56 CET 2012New York, NY, USAProceedings of the sixth international conference on Knowledge capture121--128Extracting relevant questions to an RDF dataset using formal concept analysis2011analysis concept fca formal ontology rdf semantic web With the rise of linked data, more and more semantically described information is being published online according to the principles and technologies of the Semantic Web (especially, RDF and SPARQL). The use of such standard technologies means that this data should be exploitable, integrable and reusable straight away. However, once a potentially interesting dataset has been discovered, significant efforts are currently required in order to understand its schema, its content, the way to query it and what it can answer. In this paper, we propose a method and a tool to automatically discover questions that can be answered by an RDF dataset. We use formal concept analysis to build a hierarchy of meaningful sets of entities from a dataset. These sets of entities represent answers, which common characteristics represent the clauses of the corresponding questions. This hierarchy can then be used as a querying interface, proposing questions of varying levels of granularity and specificity to the user. A major issue is however that thousands of questions can be included in this hierarchy. Based on an empirical analysis and using metrics inspired both from formal concept analysis and from ontology summarization, we devise an approach for identifying relevant questions to act as a starting point to the navigation in the question hierarchy.Ontology Merging with Formal Concept Analysishttps://puma.uni-kassel.de/bibtex/2225d908cff3ee338f7595032f236fd07/itegiteg2011-11-22T10:26:32+01:002005 itegpub l3s merging myown ontology <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Semantic Interoperability and Integration</span>, </em></span><em>Volume 04391 von Dagstuhl Seminar Proceedings, </em><em><span itemprop="publisher">IBFI, Schloss Dagstuhl, Germany</span>, </em>(<em><span>2005<meta content="2005" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011Semantic Interoperability and IntegrationDagstuhl Seminar ProceedingsOntology Merging with Formal Concept Analysis0439120052005 itegpub l3s merging myown ontology Content Aggregation on Knowledge Bases using Graph Clusteringhttps://puma.uni-kassel.de/bibtex/21788c88e04112a4491f19dfffb8dc39e/itegiteg2011-11-22T10:26:32+01:002006 aggregation clustering content graph itegpub l3s myown nepomuk ontologies ontology seminar2006 theory <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Semantic Web: Research and Applications</span>, </em></span><em>Volume 4011 von LNAI, </em><em>Seite <span itemprop="pagination">530-544</span>. </em><em>Heidelberg, </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011HeidelbergThe Semantic Web: Research and Applications530-544LNAIContent Aggregation on Knowledge Bases using Graph Clustering401120062006 aggregation clustering content graph itegpub l3s myown nepomuk ontologies ontology seminar2006 theory Recently, research projects such as PADLR and SWAP
have developed tools like Edutella or Bibster, which are targeted at
establishing peer-to-peer knowledge management (P2PKM) systems. In
such a system, it is necessary to obtain provide brief semantic
descriptions of peers, so that routing algorithms or matchmaking
processes can make decisions about which communities peers should
belong to, or to which peers a given query should be forwarded.
This paper provides a graph clustering technique on
knowledge bases for that purpose. Using this clustering, we can show
that our strategy requires up to 58% fewer queries than the
baselines to yield full recall in a bibliographic P2PKM scenario.Information Exchange and Fusion in Dynamic and Heterogeneous Distributed Environmentshttps://puma.uni-kassel.de/bibtex/23c9894e41906dc36d2e286c40d197bf8/itegiteg2011-11-22T10:26:32+01:00Heterogeneous VENUS_VS adaptation computing itegpub mobile myown ontology self-adaptive ubiquitous <meta content="thesis" itemprop="educationalUse"/><span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Roland Reichle" itemprop="url" href="/author/Roland%20Reichle"><span itemprop="name">R. Reichle</span></a></span>. </span><em>University of Kassel, Fachbereich 16: Elektrotechnik/Informatik, Distributed Systems Group, </em><em>Wilhelmshöher Allee 73, 34121 Kassel, Germany, </em>(<em><span>dez 2010<meta content="dez 2010" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011Wilhelmshöher Allee 73, 34121 Kassel, GermanydezInformation Exchange and Fusion in Dynamic and Heterogeneous Distributed Environments2010Heterogeneous VENUS_VS adaptation computing itegpub mobile myown ontology self-adaptive ubiquitous Context awareness, dynamic reconfiguration at runtime and heterogeneity are key characteristics of future distributed systems, particularly in ubiquitous and mobile computing scenarios. The main contributions of this dissertation are theoretical as well as architectural concepts facilitating information exchange and fusion in heterogeneous and dynamic distributed environments. Our main focus is on bridging the heterogeneity issues and, at the same time, considering uncertain, imprecise and unreliable sensor information in information fusion and reasoning approaches. A domain ontology is used to establish a common vocabulary for the exchanged information. We thereby explicitly support different representations for the same kind of information and provide Inter-Representation Operations that convert between them. Special account is taken of the conversion of associated meta-data that express uncertainty and impreciseness. The Unscented Transformation, for example, is applied to propagate Gaussian normal distributions across highly non-linear Inter-Representation Operations. Uncertain sensor information is fused using the Dempster-Shafer Theory of Evidence as it allows explicit modelling of partial and complete ignorance. We also show how to incorporate the Dempster-Shafer Theory of Evidence into probabilistic reasoning schemes such as Hidden Markov Models in order to be able to consider the uncertainty of sensor information when deriving high-level information from low-level data. For all these concepts we provide architectural support as a guideline for developers of innovative information exchange and fusion infrastructures that are particularly targeted at heterogeneous dynamic environments. Two case studies serve as proof of concept. The first case study focuses on heterogeneous autonomous robots that have to spontaneously form a cooperative team in order to achieve a common goal. The second case study is concerned with an approach for user activity recognition which serves as baseline for a context-aware adaptive application. Both case studies demonstrate the viability and strengths of the proposed solution and emphasize that the Dempster-Shafer Theory of Evidence should be preferred to pure probability theory in applications involving non-linear Inter-Representation Operations.A Hybrid Approach to Constructing Tag Hierarchieshttps://puma.uni-kassel.de/bibtex/2949d497bc5a29eda10c77f5784aed18b/benzbenz2011-10-18T13:46:11+02:00folksonomy learning ontology <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Geir Solskinnsbakk" itemprop="url" href="/author/Geir%20Solskinnsbakk"><span itemprop="name">G. Solskinnsbakk</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jon Gulla" itemprop="url" href="/author/Jon%20Gulla"><span itemprop="name">J. Gulla</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">On the Move to Meaningful Internet Systems, OTM 2010</span>, </em><em>Volume 6427 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin / Heidelberg, </em></span>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Tue Oct 18 13:46:11 CEST 2011Berlin / HeidelbergOn the Move to Meaningful Internet Systems, OTM 2010975-982Lecture Notes in Computer ScienceA Hybrid Approach to Constructing Tag Hierarchies64272010folksonomy learning ontology Folksonomies are becoming increasingly popular. They contain large amounts of data which can be mined and utilized for many tasks like visualization, browsing, information retrieval etc. An inherent problem of folksonomies is the lack of structure. In this paper we present an unsupervised approach for generating such structure based on a combination of association rule mining and the underlying tagged material. Using the underlying tagged material we generate a semantic representation of each tag. The semantic representation of the tags is an integral component of the structure generated. The experiment presented in this paper shows promising results with tag structures that correspond well with human judgment.SpringerLink - AbstractA Probabilistic Approach for Learning Folksonomies from Structured Datahttps://puma.uni-kassel.de/bibtex/2455bb173bb33af58bc8aaed48d8a8513/benzbenz2011-10-13T14:17:02+02:00affinity_propagation deletethistag folksonomy learning ontology <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Anon Plangprasopchok" itemprop="url" href="/author/Anon%20Plangprasopchok"><span itemprop="name">A. Plangprasopchok</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Kristina Lerman" itemprop="url" href="/author/Kristina%20Lerman"><span itemprop="name">K. Lerman</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Lise Getoor" itemprop="url" href="/author/Lise%20Getoor"><span itemprop="name">L. Getoor</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 4th ACM Web Search and Data Mining Conference</span>, </em></span>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)<em>cite arxiv:1011.3557Comment: In Proceedings of the 4th ACM Web Search and Data Mining Conference (WSDM).</em>Thu Oct 13 14:17:02 CEST 2011Proceedings of the 4th ACM Web Search and Data Mining Conferencecite arxiv:1011.3557Comment: In Proceedings of the 4th ACM Web Search and Data Mining Conference (WSDM)A Probabilistic Approach for Learning Folksonomies from Structured Data2010affinity_propagation deletethistag folksonomy learning ontology Learning structured representations has emerged as an important problem in many domains, including document and Web data mining, bioinformatics, and image analysis. One approach to learning complex structures is to integrate many smaller, incomplete and noisy structure fragments. In this work, we present an unsupervised probabilistic approach that extends affinity propagation to combine the small ontological fragments into a collection of integrated, consistent, and larger folksonomies. This is a challenging task because the method must aggregate similar structures while avoiding structural inconsistencies and handling noise. We validate the approach on a real-world social media dataset, comprised of shallow personal hierarchies specified by many individual users, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures, compared to an approach using only the standard affinity propagation algorithm. Additionally, the approach yields better overall integration quality than a state-of-the-art approach based on incremental relational clustering. A Probabilistic Approach for Learning Folksonomies from Structured DataThe State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomieshttps://puma.uni-kassel.de/bibtex/27d3c3c2189394a8686ca9812d58bfe74/hothohotho2011-09-23T16:50:02+02:00folksonomy ontology semantic tag tagging taggingsurvey toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hak Lae Kim" itemprop="url" href="/author/Hak%20Lae%20Kim"><span itemprop="name">H. Kim</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Simon Scerri" itemprop="url" href="/author/Simon%20Scerri"><span itemprop="name">S. Scerri</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="John G. Breslin" itemprop="url" href="/author/John%20G.%20Breslin"><span itemprop="name">J. Breslin</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stefan Decker" itemprop="url" href="/author/Stefan%20Decker"><span itemprop="name">S. Decker</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Hong Gee Kim" itemprop="url" href="/author/Hong%20Gee%20Kim"><span itemprop="name">H. Kim</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications</span>, </em></span><em>Seite <span itemprop="pagination">128--137</span>. </em><em>Berlin, Deutschland, </em><em><span itemprop="publisher">Dublin Core Metadata Initiative</span>, </em>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Fri Sep 23 16:50:02 CEST 2011Berlin, Deutschland{Proceedings of the 2008 International Conference on Dublin Core and Metadata Applications}128--137{The State of the Art in Tag Ontologies: A Semantic Model for Tagging and Folksonomies}2008folksonomy ontology semantic tag tagging taggingsurvey toread Semantic Network Analysis of Ontologieshttps://puma.uni-kassel.de/bibtex/22b720233e4493d4e0dee95be86dd07e8/jaeschkejaeschke2011-08-22T22:58:12+02:002006 iccs_example l3s myown ontology semantic trias_example sna analysis network social <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bettina Hoser" itemprop="url" href="/author/Bettina%20Hoser"><span itemprop="name">B. Hoser</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Schmitz" itemprop="url" href="/author/Christoph%20Schmitz"><span itemprop="name">C. Schmitz</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">The Semantic Web: Research and Applications</span>, </em><em>Volume 4011 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin/Heidelberg, </em></span><em>10.1007/11762256_38.</em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Mon Aug 22 22:58:12 CEST 2011Berlin/HeidelbergThe Semantic Web: Research and Applications10.1007/11762256_38514--529Lecture Notes in Computer ScienceSemantic Network Analysis of Ontologies401120062006 iccs_example l3s myown ontology semantic trias_example sna analysis network social A key argument for modeling knowledge in ontologies is the easy reuse and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA).While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size. A case for abductive reasoning over ontologieshttps://puma.uni-kassel.de/bibtex/215a1bdcbff44431651957f45097dc4f4/jaeschkejaeschke2011-08-08T09:24:38+02:00inference ontology reasoning <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Corinna Elsenbroich" itemprop="url" href="/author/Corinna%20Elsenbroich"><span itemprop="name">C. Elsenbroich</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Oliver Kutz" itemprop="url" href="/author/Oliver%20Kutz"><span itemprop="name">O. Kutz</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ulrike Sattler" itemprop="url" href="/author/Ulrike%20Sattler"><span itemprop="name">U. Sattler</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the OWLED*06 Workshop on OWL: Experiences and Directions</span>, </em></span><em>Volume 216 von CEUR-WS.org, </em>(<em><span>November 2006<meta content="November 2006" itemprop="datePublished"/></span></em>)Mon Aug 08 09:24:38 CEST 2011Proceedings of the OWLED*06 Workshop on OWL: Experiences and DirectionsnovCEUR-WS.orgA case for abductive reasoning over ontologies2162006inference ontology reasoning We argue for the usefulness of abductive reasoning in the context of ontologies. We discuss several applicaton scenarios in which various forms of abduction would be useful, introduce corresponding abductive reasoning tasks, give examples, and begin to develop the formal apparatus needed to employ abductive inference in expressive description logics.Automated ontology evolution in a multi-agent systemhttps://puma.uni-kassel.de/bibtex/2b5528a701397b534b3b0e5a24e37e7e2/benzbenz2011-07-07T23:23:09+02:00evolution ontology toread <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mohsen Afsharchi" itemprop="url" href="/author/Mohsen%20Afsharchi"><span itemprop="name">M. Afsharchi</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Behrouz H. Far" itemprop="url" href="/author/Behrouz%20H.%20Far"><span itemprop="name">B. Far</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 1st international conference on Scalable information systems</span>, </em></span><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Thu Jul 07 23:23:09 CEST 2011New York, NY, USAProceedings of the 1st international conference on Scalable information systemsInfoScale '06Automated ontology evolution in a multi-agent system2006evolution ontology toread This research addresses the formation of new concepts and their corresponding ontology in a multi-agent system where individual autonomous agents try to learn new concepts by consulting several other agents. In this research individual agents create and learn their distinct conceptualization and rather than a commitment to a common ontology they use their own ontologies. In this paper multi-agent supervised learning of concepts among individual agents with diverse conceptualization and different ontologies is introduced and demonstrated through an intuitive example in which supervisors are other agents rather than a human.Automated ontology evolution in a multi-agent systemAn Approach to Folksonomy-based Ontology Maintenance for Learning Environmentshttps://puma.uni-kassel.de/bibtex/2b701b92c234afa36aac87635f687cde0/benzbenz2011-06-21T13:37:04+02:00folksonomy maintenance ontology tags <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Dragan Gasevic" itemprop="url" href="/author/Dragan%20Gasevic"><span itemprop="name">D. Gasevic</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Amal Zouaq" itemprop="url" href="/author/Amal%20Zouaq"><span itemprop="name">A. Zouaq</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Carlo Torniai" itemprop="url" href="/author/Carlo%20Torniai"><span itemprop="name">C. Torniai</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Jelena Jovanovic" itemprop="url" href="/author/Jelena%20Jovanovic"><span itemprop="name">J. Jovanovic</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marek Hatala" itemprop="url" href="/author/Marek%20Hatala"><span itemprop="name">M. Hatala</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>IEEE Transactions on Learning Technologies</em></span></span> </span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Tue Jun 21 13:37:04 CEST 2011Los Alamitos, CA, USAIEEE Transactions on Learning Technologies1An Approach to Folksonomy-based Ontology Maintenance for Learning Environments992011folksonomy maintenance ontology tags An Approach to Folksonomy-based Ontology Maintenance for Learning EnvironmentsMetadata Mechanisms: From Ontology to Folksonomy ... and Backhttps://puma.uni-kassel.de/bibtex/264f809b8a18bc005c87042fc78801eae/benzbenz2011-02-17T23:23:30+01:00diploma_thesis faceted_classification folksonomy folksonomy_background ontology semantic_web tagging ol_web2.0 background widely_related <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stijn Christiaens" itemprop="url" href="/author/Stijn%20Christiaens"><span itemprop="name">S. Christiaens</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops</span>, </em></span><em><span itemprop="publisher">Springer</span>, </em>(<em><span>2006<meta content="2006" itemprop="datePublished"/></span></em>)Thu Feb 17 23:23:30 CET 2011Lecture Notes in Computer Science: On the Move to Meaningful Internet Systems 2006: OTM 2006 WorkshopsMetadata Mechanisms: From Ontology to Folksonomy ... and Back2006diploma_thesis faceted_classification folksonomy folksonomy_background ontology semantic_web tagging ol_web2.0 background widely_related In this paper we give a brief overview of different metadata mechanisms (like ontologies and folksonomies) and how they relate to each other. We identify major strengths and weaknesses of these mechanisms. We claim that these mechanisms can be classified from restricted (e.g., ontology) to free (e.g., free text tagging). In our view, these mechanisms should not be used in isolation, but rather as complementary solutions, in a continuous process wherein the strong points of one increase the semantic depth of the other. We give an overview of early active research already going on in this direction and propose that methodologies to support this process be developed. We demonstrate a possible approach, in which we mix tagging, taxonomy and ontology.