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Optimizing Search and Ranking in Folksonomy Systems by Exploiting Context Information

  • Fabian Abel
  • Nicola Henze
  • Daniel Krause
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 45)

Abstract

Tagging systems enable users to annotate resources with freely chosen keywords. The evolving bunch of tag assignments is called folksonomy and there exist already some approaches that exploit folksonomies to improve resource retrieval. In this paper, we analyze and compare graph-based ranking algorithms: FolkRank and SocialPageRank. We enhance these algorithms by exploiting the context of tags, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity itself is easy for users to perform. However, it delivers valuable semantic information about resources and their context. We present GRank that uses the context information to improve and optimize the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.

Keywords

Social media Folksonomy systems Search Ranking Optimization FolkRank GFolkRank SocialPageRank GRank 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fabian Abel
    • 1
  • Nicola Henze
    • 1
  • Daniel Krause
    • 1
  1. 1.IVS – Semantic Web GroupLeibniz University HannoverHannoverGermany

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