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Discovering Shared Conceptualizations in Folksonomies

by: Robert Jäschke and Andreas Hotho and Christoph Schmitz and Bernhard Ganter and Gerd Stumme

In: Web Semantics: Science, Services and Agents on the World Wide Web , Vol. 6 , Nr. 1New York: Elsevier , Feb (2008) , p. 38--53.

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URL:	http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b

Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.

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ScienceDirect - Web Semantics: Science, Services and Agents on the World Wide Web : Discovering shared conceptualizations in folksonomies

BibTeX record

@article{jaeschke2008discovering,
  address = {New York},
  author = {Robert Jäschke and Andreas Hotho and Christoph Schmitz and Bernhard Ganter and Gerd Stumme},
  booktitle = {Semantic Web and Web 2.0},
  editor = {T. Finin and R. Mizoguchi and S. Staab},
  interHash = {cfca594f9dbe30694bfbcdeb40dc4e88},
  intraHash = {18e8babe208fae2c0342438617b0ec31},
  journal = {Web Semantics: Science, Services and Agents on the World Wide Web},
  number = {1},
  pages = {38--53},
  publisher = {Elsevier},
  title = {Discovering Shared Conceptualizations in Folksonomies},
  url = {http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b},
  volume = {6},
  year = {2008},
  issn = {1570-8268},
  vgwort = {59},
  doi = {10.1016/j.websem.2007.11.004},
  month = Feb,
  abstract = {Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.}
}

Endnote record

%0 Journal Article
%1 jaeschke2008discovering
%A Robert Jäschke
%A Andreas Hotho
%A Christoph Schmitz
%A Bernhard Ganter
%A Gerd Stumme
%B Semantic Web and Web 2.0
%C New York
%D 2008
%E T. Finin
%E R. Mizoguchi
%E S. Staab
%I Elsevier
%J Web Semantics: Science, Services and Agents on the World Wide Web
%K 
%N 1
%P 38--53
%T Discovering Shared Conceptualizations in Folksonomies
%R 10.1016/j.websem.2007.11.004
%U http://www.sciencedirect.com/science/article/B758F-4R53WD4-1/2/ae56bd6e7132074272ca2035be13781b
%V 6
%X Social bookmarking tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. Unlike ontologies, shared conceptualizations are not formalized, but rather implicit. We present a new data mining task, the mining of all frequent tri-concepts, together with an efficient algorithm, for discovering these implicit shared conceptualizations. Our approach extends the data mining task of discovering all closed itemsets to three-dimensional data structures to allow for mining folksonomies. We provide a formal definition of the problem, and present an efficient algorithm for its solution. Finally, we show the applicability of our approach on three large real-world examples.