Mining Association Rules in Folksonomies
C. Schmitz, A. Hotho, R. J�schke, and G. Stumme.
Data Science and Classification. Proceedings of the 10th IFCS Conf., page 261--270. Heidelberg, Springer, (July 2006)

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.

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@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.},
  added-at = {2011-02-04T16:09:39.000+0100},
  address = {Heidelberg},
  author = {Schmitz, Christoph and Hotho, Andreas and J�schke, Robert and Stumme, Gerd},
  biburl = {https://puma.uni-kassel.de/bibtex/291a9a847b72a77e8f7d7db4de52716e5/benz},
  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},
  keywords = {ol_web2.0 nepomuk closely_related semantic folksonomy diploma_thesis analysis network},
  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 = {2011-02-04T16:09:39.000+0100},
  title = {Mining Association Rules in Folksonomies},
  year = 2006
}

%0 Conference Paper
%1 schmitz2006mining
%A Schmitz, Christoph
%A Hotho, Andreas
%A J�schke, Robert
%A Stumme, Gerd
%B Data Science and Classification. Proceedings of the 10th IFCS Conf.
%C Heidelberg
%D 2006
%E Batagelj, V.
%E Bock, H.-H.
%E Ferligoj, A.
%E �iberna, A.
%I Springer
%K ol_web2.0 nepomuk closely_related semantic folksonomy diploma_thesis analysis network
%P 261--270
%T Mining Association Rules in Folksonomies
%X 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.

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Mining Association Rules in Folksonomies
C. Schmitz, A. Hotho, R. J�schke, and G. Stumme.
Data Science and Classification. Proceedings of the 10th IFCS Conf., page 261--270. Heidelberg, Springer, (July 2006)

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PUMA

Mining Association Rules in FolksonomiesC. Schmitz, A. Hotho, R. J�schke, and G. Stumme. Data Science and Classification. Proceedings of the 10th IFCS Conf., page 261--270. Heidelberg, Springer, (July 2006)

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Cite this publication

Mining Association Rules in Folksonomies
C. Schmitz, A. Hotho, R. J�schke, and G. Stumme.
Data Science and Classification. Proceedings of the 10th IFCS Conf., page 261--270. Heidelberg, Springer, (July 2006)