@proceedings{stumme05semanticnetworkanalysis,
  address = {Aachen},
  editor = {Stumme, Gerd and Hoser, Bettina and Schmitz, Christoph and Alani, Harith},
  interhash = {6316cb226778a6a6f156821f975b2ba3},
  intrahash = {c44763991d44182c53606a2c93054f26},
  issn = {1613-0073},
  publisher = {CEUR Proceedings},
  title = {Proceedings of the First Workshop on Semantic Network Analysis },
  url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-171/},
  year = 2005
}

@inproceedings{schmitz2006mining,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.},
  editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and �iberna, A.},
  file = {schmitz2006mining.pdf:schmitz2006mining.pdf:PDF},
  groups = {public},
  interhash = {9f407e0b779aba5b3afca7fb906f579b},
  intrahash = {ed504c16bc4eb561a9446bd98b10dca1},
  lastdatemodified = {2006-12-07},
  lastname = {Schmitz},
  month = {July},
  own = {notown},
  pages = {261--270},
  pdf = {schmitz06-mining.pdf},
  publisher = {Springer},
  read = {notread},
  series = {Studies in Classification, Data Analysis, and Knowledge Organization},
  timestamp = {2007-09-11 13:31:35},
  title = {Mining Association Rules in Folksonomies},
  username = {dbenz},
  year = 2006
}

@inproceedings{schmitz2006mining,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. These systems provide currently relatively few structure. We discuss in this paper, how association rule mining can be adopted to analyze and structure folksonomies, and how the results can be used for ontology learning and supporting emergent semantics. We demonstrate our approach on a large scale dataset stemming from an online system.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and J�schke, Robert and Stumme, Gerd},
  booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.},
  editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and �iberna, A.},
  file = {schmitz2006mining.pdf:schmitz2006mining.pdf:PDF},
  interhash = {9f407e0b779aba5b3afca7fb906f579b},
  intrahash = {91a9a847b72a77e8f7d7db4de52716e5},
  lastdatemodified = {2006-12-07},
  lastname = {Schmitz},
  month = {July},
  own = {notown},
  pages = {261--270},
  pdf = {schmitz06-mining.pdf},
  publisher = {Springer},
  read = {notread},
  series = {Studies in Classification, Data Analysis, and Knowledge Organization},
  title = {Mining Association Rules in Folksonomies},
  year = 2006
}

@inproceedings{hoser2006semantic,
  abstract = {A key argument for modeling knowledge in ontologies is the easy
re-use and re-engineering of the knowledge. However, beside
consistency checking, current ontology engineering tools provide
only basic functionalities for analyzing ontologies. Since
ontologies can be considered as (labeled, directed) 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 in general currently receive
high attention in the Semantic Web community, there are only very
few SNA applications up to now, and virtually none for analyzing the
structure of ontologies.

We illustrate in this paper 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 based on Hermitian matrices, we illustrate
the insights these measures provide on two ontologies, which are
different in purpose, scope, and size.},
  address = {Heidelberg},
  author = {Hoser, Bettina and Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {The Semantic Web: Research and Applications},
  editor = {Sure, York and Domingue, John},
  interhash = {344ec3b4ee8af1a2c6b86efc14917fa9},
  intrahash = {c0cdbeab23ce0fc1bff5e02c99aab012},
  month = {June},
  pages = {514-529},
  publisher = {Springer},
  series = {LNAI},
  title = {Semantic Network Analysis of Ontologies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hoser2006semantic.pdf},
  volume = 4011,
  year = 2006
}

@proceedings{alani2006proceedings,
  editor = {Alani, Harith and Hoser, Bettina and Schmitz, Christoph and Stumme, Gerd},
  interhash = {e991143409a8f4acb9eabfe08a38e387},
  intrahash = {786a452a14c5189d82dc56f16cc2a266},
  title = {Proceedings of the 2nd Workshop on Semantic Network Analysis},
  url = {http://www.kde.cs.uni-kassel.de/ws/sna2006/},
  year = 2006
}

@inproceedings{schmitz2006mining,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such
systems users are setting up lightweight conceptual structures
called folksonomies. These systems provide currently relatively few
structure. We discuss in this paper, how association rule mining
can be adopted to analyze and structure folksonomies, and how the results can be used
for ontology learning and supporting emergent semantics. We
demonstrate our approach on a large scale dataset stemming from an
online system.},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Data Science and Classification. Proceedings of the 10th IFCS Conf.},
  editor = {Batagelj, V. and Bock, H.-H. and Ferligoj, A. and Žiberna, A.},
  interhash = {20650d852ca3b82523fcd8b63e7c12d7},
  intrahash = {11b2a59a568d246d7f36cb68169a464a},
  month = {July},
  pages = {261--270},
  publisher = {Springer},
  series = {Studies in Classification, Data Analysis, and Knowledge Organization},
  title = {Mining Association Rules in Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/schmitz2006mining.pdf},
  year = 2006
}

@proceedings{stumme05semanticnetworkanalysis,
  address = {Aachen},
  editor = {Stumme, Gerd and Hoser, Bettina and Schmitz, Christoph and Alani, Harith},
  interhash = {6316cb226778a6a6f156821f975b2ba3},
  intrahash = {c44763991d44182c53606a2c93054f26},
  issn = {1613-0073},
  publisher = {CEUR Proceedings},
  title = {Proceedings of the First Workshop on Semantic Network Analysis },
  url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-171/},
  year = 2005
}

@inproceedings{groza2007nepomuk,
  abstract = {This paper introduces the NEPOMUK project which aims to create a standard and reference implementation for the Social Semantic Desktop. We outline the requirements and functionalities that were identified for a useful Semantic Desktop system and present an architecture that fulfills these requirements which was acquired by incremental refinement of the architecture of existing Semantic Desktop prototypes. The NEPOMUK project is primarily motivated by three real-life industrial use-cases, we briefly outline these and the processes used to extract required functionalities from the people working in these areas today, and we present a selection of typical tasks where the Semantic Desktop could be of benefit.},
  author = {Groza, Tudor and Handschuh, Siegfried and Moeller, Knud and Grimnes, Gunnar and Sauermann, Leo and Minack, Enrico and Mesnage, Cedric and Jazayeri, Mehdi and Reif, Gerald and Gudjónsdóttir, Rosa},
  booktitle = {Proceedings of I-Semantics' 07},
  editor = {Pellegrini, Tassilo and Schaffert, Sebastian},
  interhash = {b56d7a56970270747d16b89585f1370d},
  intrahash = {6a466091e1478277aeeffe1e094e48b1},
  issn = {0948-6968},
  month = sep,
  pages = {201--211},
  publisher = {JUCS},
  title = {The NEPOMUK Project - On the way to the Social Semantic Desktop},
  url = {http://www.dfki.uni-kl.de/~sauermann/papers/groza+2007a.pdf},
  year = 2007
}

@inbook{schmitz2006kollaboratives,
  abstract = {Wissensmanagement in zentralisierten Wissensbasen erfordert
einen hohen Aufwand für Erstellung und Wartung, und es entspricht nicht
immer den Anforderungen der Benutzer. Wir geben in diesem Kapitel einen Überblick
über zwei aktuelle Ansätze, die durch kollaboratives Wissensmanagement
diese Probleme lösen können. Im Peer-to-Peer-Wissensmanagement unterhalten
Benutzer dezentrale Wissensbasen, die dann vernetzt werden können, um
andere Benutzer eigene Inhalte nutzen zu lassen. Folksonomies versprechen, die
Wissensakquisition so einfach wie möglich zu gestalten und so viele Benutzer in
den Aufbau und die Pflege einer gemeinsamen Wissensbasis einzubeziehen.},
  author = {Schmitz, Christoph and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd},
  booktitle = {Semantic Web - Wege zur vernetzten Wissensgesellschaft},
  editor = {Pellegrini, Tassilo and Blumauer, Andreas},
  interhash = {cc0f3d4fa8f36968f02837e3f9f5c57b},
  intrahash = {53e13744981f2c04d9239e0cf9b4e689},
  isbn = {3-540-29324-8},
  pages = {273-290},
  publisher = {Springer},
  title = {Kollaboratives Wissensmanagement},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006kollaboratives.pdf},
  year = 2006
}