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Automatic Tag Suggestion Based on Resource Contents

  • Bruno Oliveira
  • Pável Calado
  • H. Sofia Pinto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5268)

Abstract

Although social tagging systems are becoming increasingly popular, tagging is still usually a manual process. When publishing on a social tagging system, the user is asked for the tags he wishes to assign to the resource being made available. In this paper, we present an automatic tag suggester, Tess. Our system makes recommendations based only on the textual contents of the resource and is independent of existing tags, thus allowing the emergence of novel tags. The system was evaluated by a group of users and statistical measures were applied to infer its performance. Results show that the system is not only able to suggest many useful tags, but also to discover new and relevant tags, not suggested by any of the human users.

Keywords

Textual Content Term Weight Inverse Document Frequency Similar Document Query Vector 
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 2008

Authors and Affiliations

  • Bruno Oliveira
    • 1
  • Pável Calado
    • 1
  • H. Sofia Pinto
    • 1
  1. 1.Instituto Superior Técnico/INESC-IDLisboaPortugal

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