@incollection{hoser2006semantic, abstract = { 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. }, address = {Berlin/Heidelberg}, author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, doi = {10.1007/11762256_38}, editor = {Sure, York and Domingue, John}, interhash = {344ec3b4ee8af1a2c6b86efc14917fa9}, intrahash = {2b720233e4493d4e0dee95be86dd07e8}, isbn = {978-3-540-34544-2}, note = {10.1007/11762256_38}, pages = {514--529}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Semantic Network Analysis of Ontologies}, url = {http://dx.doi.org/10.1007/11762256_38}, volume = 4011, year = 2006 } @inproceedings{tane03courseware, abstract = {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.}, author = {Tane, Julien and Schmitz, Christoph and Stumme, Gerd and Staab, Steffen and Studer, R.}, booktitle = {Mobiles Lernen und Forschen - Beiträge der Fachtagung an der Universität}, comment = {alpha}, editor = {David, Klaus and Wegner, Lutz}, interhash = {7f33080bb78d089b24bf51c059f8f018}, intrahash = {850949481723b7dd03768ccd96b25cb9}, month = {November}, pages = {93-104}, publisher = {Kassel University Press}, title = {The Courseware Watchdog: an Ontology-based tool for finding and organizing learning material}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/tane2003courseware.pdf}, year = 2003 } @inproceedings{hotho03ontologies, address = {Melbourne, Florida}, author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd}, booktitle = {Proceedings of the 2003 IEEE International Conference on Data Mining}, comment = {alpha}, interhash = {b56c36d6d9c9ca9e6bd236a0f92415a5}, intrahash = {57a39c81cff1982dbefed529be934bee}, month = {November 19-22,}, pages = {541-544 (Poster}, publisher = {IEEE {C}omputer {S}ociety}, title = {Ontologies improve text document clustering}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003ontologies.pdf}, year = 2003 } @inproceedings{Hepp:2006:HWC, abstract = {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.}, author = {Hepp, Martin and Bachlechner, Daniel and Siorpaes, Katharina}, booktitle = {Proceedings of the First Workshop on Semantic Wikis -- From Wiki To Semantics}, crossref = {SemWiki2006-proceedings}, editor = {V\"{o}lkel, Max and Schaffert, Sebastian}, interhash = {640e549f082756c3272467c9df64307e}, intrahash = {d120c5c498e9cec5ce2934537c58978f}, month = {June}, owner = {voelkel}, publisher = {ESWC2006}, series = {Workshop on Semantic Wikis}, timestamp = {2006.06.14}, title = {Harvesting Wiki Consensus - Using Wikipedia Entries as Ontology Elements}, url = {http://semwiki.org/semwiki2006}, year = 2006 } @inproceedings{hotho03explaining, abstract = {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.}, address = {Heidelberg}, author = {Hotho, Andreas and Staab, Steffen and Stumme, Gerd}, booktitle = {Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases}, comment = {alpha}, editor = {Lavra\v{c}, Nada and Gamberger, Dragan and Todorovski, Hendrik BlockeelLjupco}, interhash = {cf66183151a5d94a0941ac6d5089ae89}, intrahash = {53a943b6be4b34cf4e5329d0b58e99f6}, pages = {217-228}, publisher = {Springer}, series = {LNAI}, title = {Explaining Text Clustering Results using Semantic Structures}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/hotho2003explaining.pdf}, volume = 2838, year = 2003 } @inproceedings{studer03building, address = {Osaka, Japan}, author = {Studer, Rudi and Stumme, Gerd and Handschuh, Siegfried and Hotho, Andreas and Motik, B.}, booktitle = {New Trends in Knowledge Processing -- Data Mining, Semantic Web and Computational}, comment = {alpha}, interhash = {67d164f4a531ddf0f84df0b5de52e80a}, intrahash = {a0e7b52680f1876cdd9cd21f7cb2f95c}, month = {March 10-11,}, pages = {31-34}, title = {Building and Using the Semantic Web}, url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2003/Sanken03.pdf}, year = 2003 }