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Data Mining on Folksonomies

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Intelligent Information Access

Part of the book series: Studies in Computational Intelligence ((SCI,volume 301))

Abstract

Social resource sharing systems are central elements of the Web 2.0 and use all the same kind of lightweight knowledge representation, called folksonomy. As these systems are easy to use, they attract huge masses of users. Data Mining provides methods to analyze data and to learn models which can be used to support users. The application and adaptation of known data mining algorithms to folksonomies with the goal to support the users of such systems and to extract valuable information with a special focus on the Semantic Web is the main target of this paper.

In this work we give a short introduction into folksonomies with a focus on our own system BibSonomy. Based on the analysis we made on a large folksonomy dataset, we present the application of data mining algorithms on three different tasks, namely spam detection, ranking and recommendation. To bridge the gap between folksonomies and the Semantic Web, we apply association rule mining to extract relations and present a deeper analysis of statistical measures which can be used to extract tag relations. This approach is complemented by presenting two approaches to extract conceptualizations from folksonomies.

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Author information

Authors and Affiliations

  1. Knowledge & Data Engineering Group, University of Kassel, 34121, Kassel, Germany

    Andreas Hotho

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  1. Andreas Hotho
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Editor information

Editors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari Piazza d’Armi , I09123, Cagliari, Italy

    Giuliano Armano  & Eloisa Vargiu  & 

  2. Department of Informatics, University of Bari “Aldo Moro” , Via E. Orabona, 4, 70126, Bari, Italy

    Marco de Gemmis

  3. Department of Informatics, University of Bari “AldoMoro” , Via E. Orabona, 4, 70126, Bari, Italy

    Giovanni Semeraro

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Hotho, A. (2010). Data Mining on Folksonomies. In: Armano, G., de Gemmis, M., Semeraro, G., Vargiu, E. (eds) Intelligent Information Access. Studies in Computational Intelligence, vol 301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14000-6_4

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  • DOI: https://doi.org/10.1007/978-3-642-14000-6_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13999-4

  • Online ISBN: 978-3-642-14000-6

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