http://www.bibsonomy.org/burst/user/jaeschke/ontologyBibSonomy BuRST Feed for /user/jaeschke/ontology2008-05-14T09:51:54+02:00http://www.bibsonomy.org/bibtex/25a9065e96237a69d95edebc03ccac92d/jaeschkejaeschke2007-05-16T09:38:12+02:00flickr folksonomy ontology <span style="color:#555555;">Patrick <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> </span><em>Collaborative Web Tagging Workshop at WWW 2006, </em><em>Edinburgh, Scotland, </em><em>May2006. </em>Edinburgh, ScotlandCollaborative Web Tagging Workshop at WWW 2006MayInducing Ontology from Flickr Tags.2006flickrfolksonomyontologyhttp://www.bibsonomy.org/bibtex/29a2c77c7c7a1b19cd16df08cca65f706/jaeschkejaeschke2007-02-01T14:04:37+01:002006 analysis iccs_example l3s myown network ontology semantic trias_example <span style="color:#555555;">Bettina <a href="http://www.bibsonomy.org/author/Hoser">Hoser</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> und Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>The Semantic Web: Research and Applications, </em><em>Springer, </em><em>June2006. </em><em>Proceedings of the 3rd European Semantic Web Conference, Budva, Montenegro . </em>The Semantic Web: Research and ApplicationsJuneProceedings of the 3rd European Semantic Web Conference, Budva, MontenegroLecture Notes in Computer ScienceSemantic Network Analysis of Ontologies20062006analysisiccs_examplel3smyownnetworkontologysemantictrias_exampleA key argument for modeling knowledge in ontologies is the easy re-use 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.http://www.bibsonomy.org/bibtex/2850949481723b7dd03768ccd96b25cb9/jaeschkejaeschke2007-02-01T14:04:37+01:00iccs_example learning ontology tool trias_example <span style="color:#555555;">Julien <a href="http://www.bibsonomy.org/author/Tane">Tane</a> und Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> und Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> und R. <a href="http://www.bibsonomy.org/author/Studer">Studer</a> </span><em>Mobiles Lernen und Forschen - Beitr&#228;ge der Fachtagung an der Universit&#228;t, </em><em>Seite93-104. </em><em>Kassel University Press, </em><em>November2003. </em>Mobiles Lernen und Forschen - Beiträge der Fachtagung an der UniversitätNovember93-104The Courseware Watchdog: an Ontology-based tool for finding and organizing learning material2003iccs_examplelearningontologytooltrias_exampleTopics in education are changing with an ever faster pace. E-Learning resources tend to be more and more decentralised. Users need increasingly to be able to use the resources of the web. For this, they should have tools for finding and organizing information in a decentral way. In this, paper, we show how an ontology-based tool suite allows to make the most of the resources available on the web.http://www.bibsonomy.org/bibtex/257a39c81cff1982dbefed529be934bee/jaeschkejaeschke2007-02-01T14:04:37+01:00clustering iccs_example ontology text trias_example <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the 2003 IEEE International Conference on Data Mining, </em><em>Seite541-544 (Poster. </em><em>Melbourne, Florida, </em><em>IEEE Computer Society, </em><em>November 19-22,2003. </em>Melbourne, FloridaProceedings of the 2003 IEEE International Conference on Data MiningNovember 19-22,541-544 (PosterOntologies improve text document clustering2003clusteringiccs_exampleontologytexttrias_exampleAll references containing the term "wiki" from the bibtex files of papers on the SemWiki2006 WShttp://www.bibsonomy.org/bibtex/2d120c5c498e9cec5ce2934537c58978f/jaeschkejaeschke2007-02-01T14:04:37+01:00iccs_example ontology semantic trias_example web wiki wikipedia <span style="color:#555555;">Martin <a href="http://www.bibsonomy.org/author/Hepp">Hepp</a> und Daniel <a href="http://www.bibsonomy.org/author/Bachlechner">Bachlechner</a> und Katharina <a href="http://www.bibsonomy.org/author/Siorpaes">Siorpaes</a> </span><em>Proceedings of the First Workshop on Semantic Wikis -- From Wiki To Semantics, </em><em>ESWC2006, </em><em>June2006. </em>Proceedings of the First Workshop on Semantic Wikis -- From Wiki To SemanticsSemWiki2006-proceedingsJuneWorkshop on Semantic WikisHarvesting Wiki Consensus - Using Wikipedia Entries as Ontology Elements2006iccs_exampleontologysemantictrias_examplewebwikiwikipediaOne major obstacle towards adding machine-readable annotation to existing Web content is the lack of domain ontologies. While FOAF and Dublin Core are popular means for expressing relationships between Web resources and between Web resources and literal values, we widely lack unique identifiers for common concepts and instances. Also, most available ontologies have a very weak community grounding in the sense that they are designed by single individuals or small groups of individuals, while the majority of potential users is not involved in the process of proposing new ontology elements or achieving consensus. This is in sharp contrast to natural language where the evolution of the vocabulary is under the control of the user community. At the same time, we can observe that, within Wiki communities, especially Wikipedia, a large number of users is able to create comprehensive domain representations in the sense of unique, machine-feasible, identifiers and concept definitions which are sufficient for humans to grasp the intension of the concepts. The English version of Wikipedia contains now more than one million entries and thus the same amount of URIs plus a human-readable description. While this collection is on the lower end of ontology expressiveness, it is likely the largest living ontology that is available today. In this paper, we (1) show that standard Wiki technology can be easily used as an ontology development environment for named classes, reducing entry barriers for the participation of users in the creation and maintenance of lightweight ontologies, (2) prove that the URIs of Wikipedia entries are surprisingly reliable identifiers for ontology concepts, and (3) demonstrate the applicability of our approach in a use case.http://www.bibsonomy.org/bibtex/253a943b6be4b34cf4e5329d0b58e99f6/jaeschkejaeschke2007-02-01T14:04:37+01:00clustering concept fca formal iccs_example ontology text trias_example <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und Steffen <a href="http://www.bibsonomy.org/author/Staab">Staab</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, </em><em>Volume2838vonLNAI, </em><em>Seite217-228. </em><em>Heidelberg, </em><em>Springer, </em>(<em>2003</em>)HeidelbergKnowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases217-228LNAIExplaining Text Clustering Results using Semantic Structures28382003clusteringconceptfcaformaliccs_exampleontologytexttrias_exampleCommon text clustering techniques offer rather poor capabilities for explaining to their users why a particular result has been achieved. They have the disadvantage that they do not relate semantically nearby terms and that they cannot explain how resulting clusters are related to each other. In this paper, we discuss a way of integrating a large thesaurus and the computation of lattices of resulting clusters into common text clustering in order to overcome these two problems. As its major result, our approach achieves an explanation using an appropriate level of granularity at the concept level as well as an appropriate size and complexity of the explaining lattice of resulting clusters.http://www.bibsonomy.org/bibtex/2a0e7b52680f1876cdd9cd21f7cb2f95c/jaeschkejaeschke2007-02-01T14:04:37+01:00iccs_example mining ontology semantic trias_example web <span style="color:#555555;">Rudi <a href="http://www.bibsonomy.org/author/Studer">Studer</a> und Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> und Siegfried <a href="http://www.bibsonomy.org/author/Handschuh">Handschuh</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> und B. <a href="http://www.bibsonomy.org/author/Motik">Motik</a> </span><em>New Trends in Knowledge Processing -- Data Mining, Semantic Web and Computational, </em><em>Seite31-34. </em><em>Osaka, Japan, </em><em>March 10-11,2003. </em>Osaka, JapanNew Trends in Knowledge Processing -- Data Mining, Semantic Web and ComputationalMarch 10-11,31-34Building and Using the Semantic Web2003iccs_exampleminingontologysemantictrias_examplewebhttp://www.bibsonomy.org/bibtex/2923fe2e2d6a389c74c679d4f1f9a2a5e/jaeschkejaeschke2006-04-20T15:01:04+02:00ontocopi pagerank ontology analysis network ranking <span style="color:#555555;">Harith <a href="http://www.bibsonomy.org/author/Alani">Alani</a> und Srinandan <a href="http://www.bibsonomy.org/author/Dasmahapatra">Dasmahapatra</a> und Kieron <a href="http://www.bibsonomy.org/author/O&#039;Hara">O'Hara</a> und Nigel <a href="http://www.bibsonomy.org/author/Shadbolt">Shadbolt</a> </span><em>IEEE Intelligent Systems</em><em>18(2):18-25</em><em>March/April2003. </em>IEEE Intelligent SystemsMarch/April218-25{I}dentifying {C}ommunities of {P}ractice through {O}ntology {N}etwork {A}nalysis182003ontocopipagerankontologyanalysisnetworkrankinghttp://www.bibsonomy.org/bibtex/2e73e27d62d17749a8ddba7aab2126cb7/jaeschkejaeschke2006-04-19T21:20:03+02:00social folksonomy ontology tagging seminar2006 <span style="color:#555555;">Peter <a href="http://www.bibsonomy.org/author/Mika">Mika</a> </span><em>Proceedings of the 4th International Semantic Web Conference, </em><em>Volume3729vonLecture Notes in Computer Science, </em><em>Seite522-536. </em><em>Springer, </em>(<em>2005</em>)Proceedings of the 4th International Semantic Web Conference522-536Lecture Notes in Computer ScienceOntologies Are Us: A Unified Model of Social Networks and Semantics37292005socialfolksonomyontologytaggingseminar2006http://www.bibsonomy.org/bibtex/25668aeea53c496d92b8d73c1a16ede2d/jaeschkejaeschke2006-04-11T08:36:27+02:00ontology nepomuk dbus rdf <span style="color:#555555;">Joe <a href="http://www.bibsonomy.org/author/Geldart">Geldart</a> </span><em>April2005. </em>AprilRDF without Revolution An Analysis and Test of RDF and Ontology Bachelor Thesis2005ontologynepomukdbusrdfThis dissertation describes the design and development of the Frege shared information system. This system builds upon the work of semantic desktop systems such as Gnowsis and Haystack, exploring the ways that ontological information may be integrated into an existing desktop environment. The major contribution of this work is the introduction of the idea of ‘reflections’ between information models as a formal basis for integrating a shared information system with existing applications. The success of this work is intended to be judged by its ease of use for developers, the completeness of the model reflection and its efficiency. According to these criteria the design implemented may be judged a partial success, achieving an easy-to-use reflection which is practically too slow to use in general purpose systems. The work does, however, suggest means to improve this in future systems in order to bring about a fully-integrated, evolutionary semantic desktop system.