%0 
%0 Journal Article
%A Alani, Harith; Dasmahapatra, Srinandan; O'Hara, Kieron & Shadbolt, Nigel
%D 2003
%T Identifying Communities of Practice through Ontology Network Analysis
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%B IEEE Intelligent Systems
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%V 18
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%N 2
%P 18-25
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%K analysis network ontocopi ontology pagerank ranking 
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%0 
%0 Thesis
%A Geldart, Joe
%D 2005
%T RDF without Revolution An Analysis and Test of RDF and Ontology 
%E 
%B 
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%I Department of Computer Science, University of Durham
%V 
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%8 April
%9 Bachelor Thesis
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%F Geldart2005
%K dbus nepomuk ontology rdf 
%X This 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.
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%U http://www.dur.ac.uk/j.r.c.geldart/projects/frege/docs/report.pdf
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%0 
%0 Conference Proceedings
%A Hepp, Martin; Bachlechner, Daniel & Siorpaes, Katharina
%D 2006
%T Harvesting Wiki Consensus - Using Wikipedia Entries as Ontology Elements
%E V\"o,lkel, Max & Schaffert, Sebastian
%B Proceedings of the First Workshop on Semantic Wikis -- From Wiki To Semantics
%C 
%I ESWC2006
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%S Workshop on Semantic Wikis
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%4 SemWiki2006-proceedings
%# voelkel
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%F Hepp:2006:HWC
%K iccs_example ontology semantic trias_example web wiki wikipedia 
%X One 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.
%Z 
%U http://semwiki.org/semwiki2006
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%0 
%0 Conference Proceedings
%A Hoser, Bettina; Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph & Stumme, Gerd
%D 2006
%T Semantic Network Analysis of Ontologies
%E 
%B The Semantic Web: Research and Applications
%C 
%I Springer
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%N 
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%S Lecture Notes in Computer Science
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%8 June
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%F hoser2006semantic
%K 2006 analysis iccs_example l3s myown network ontology semantic trias_example 
%X A 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.
%Z Proceedings of the 3rd European Semantic Web Conference, Budva, Montenegro
%U 
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%0 
%0 Conference Proceedings
%A Hotho, Andreas; Staab, Steffen & Stumme, Gerd
%D 2003
%T Explaining Text Clustering Results using Semantic Structures
%E Lavra\vc,, Nada; Gamberger, Dragan & Todorovski, Hendrik BlockeelLjupco
%B Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases
%C Heidelberg
%I Springer
%V 2838
%6
%N 
%P 217-228
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%S LNAI
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%F hotho03explaining
%K clustering concept fca formal iccs_example ontology text trias_example 
%X Common 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.
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%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf
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%0 
%0 Conference Proceedings
%A Hotho, Andreas; Staab, Steffen & Stumme, Gerd
%D 2003
%T Ontologies improve text document clustering
%E 
%B Proceedings of the 2003 IEEE International Conference on Data Mining
%C Melbourne, Florida
%I IEEE {C}omputer {S}ociety
%V 
%6
%N 
%P 541-544 (Poster
%& 
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%S 
%7
%8 November 19-22,
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%F hotho03ontologies
%K clustering iccs_example ontology text trias_example 
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%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003ontologies.pdf
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%0 
%0 Conference Proceedings
%A Mika, Peter
%D 2005
%T Ontologies Are Us: A Unified Model of Social Networks and Semantics
%E 
%B Proceedings of the 4th International Semantic Web Conference
%C 
%I Springer
%V 3729
%6
%N 
%P 522-536
%& 
%Y 
%S Lecture Notes in Computer Science
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%F mika05ontologies
%K folksonomy ontology seminar2006 social tagging 
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%U http://dblp.uni-trier.de/db/conf/semweb/iswc2005.html#Mika05
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%0 
%0 Conference Proceedings
%A Schmitz, Patrick
%D 2006
%T Inducing Ontology from Flickr Tags.
%E 
%B Collaborative Web Tagging Workshop at WWW 2006
%C Edinburgh, Scotland
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%K flickr folksonomy ontology 
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%0 Conference Proceedings
%A Studer, Rudi; Stumme, Gerd; Handschuh, Siegfried; Hotho, Andreas & Motik, B.
%D 2003
%T Building and Using the Semantic Web
%E 
%B New Trends in Knowledge Processing -- Data Mining, Semantic Web and Computational
%C Osaka, Japan
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%6
%N 
%P 31-34
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%8 March 10-11,
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%K iccs_example mining ontology semantic trias_example web 
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%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/Sanken03.pdf
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%0 
%0 Conference Proceedings
%A Tane, Julien; Schmitz, Christoph; Stumme, Gerd; Staab, Steffen & Studer, R.
%D 2003
%T The Courseware Watchdog: an Ontology-based tool for finding and organizing learning material
%E David, Klaus & Wegner, Lutz
%B Mobiles Lernen und Forschen - Beiträge der Fachtagung an der Universität
%C 
%I Kassel University Press
%V 
%6
%N 
%P 93-104
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%8 November
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%F tane03courseware
%K iccs_example learning ontology tool trias_example 
%X Topics 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.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2003/tane2003courseware.pdf
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