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The Impact of Multifaceted Tagging on Learning Tag Relations and Search

  • Fabian Abel
  • Nicola Henze
  • Ricardo Kawase
  • Daniel Krause
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6089)

Abstract

In this paper we present a model for multifaceted tagging, i.e. tagging enriched with contextual information. We present TagMe!, a social tagging front-end for Flickr images, that provides multifaceted tagging functionality: It enables users to attach tag assignments to a specific area within an image and to categorize tag assignments. Moreover, TagMe! maps tags and categories to DBpedia URIs to clearly define the meaning of freely-chosen words. Our experiments reveal the benefits of these additional tagging facets. For example, the exploitation of the facets significantly improves the performance of FolkRank-based search. Further, we demonstrate the benefits of TagMe! tagging facets for learning semantics within folksonomies.

Keywords

Ranking Algorithm Keyword Query Ranking Strategy Context Type Additional Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fabian Abel
    • 1
  • Nicola Henze
    • 1
  • Ricardo Kawase
    • 1
  • Daniel Krause
    • 1
  1. 1.IVS – Semantic Web Group & L3S Research CenterLeibniz University HannoverGermany

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