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Folksonomy-Based Collabulary Learning

  • Leandro Balby Marinho
  • Krisztian Buza
  • Lars Schmidt-Thieme
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5318)

Abstract

The growing popularity of social tagging systems promises to alleviate the knowledge bottleneck that slows down the full materialization of the Semantic Web since these systems allow ordinary users to create and share knowledge in a simple, cheap, and scalable representation, usually known as folksonomy. However, for the sake of knowledge workflow, one needs to find a compromise between the uncontrolled nature of folksonomies and the controlled and more systematic vocabulary of domain experts. In this paper we propose to address this concern by devising a method that automatically enriches a folksonomy with domain expert knowledge and by introducing a novel algorithm based on frequent itemset mining techniques to efficiently learn an ontology over the enriched folksonomy. In order to quantitatively assess our method, we propose a new benchmark for task-based ontology evaluation where the quality of the ontologies is measured based on how helpful they are for the task of personalized information finding. We conduct experiments on real data and empirically show the effectiveness of our approach.

Keywords

Association Rule Recommender System Frequent Itemset Semantic Mapping Frequent Itemset Mining 
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

  • Leandro Balby Marinho
    • 1
  • Krisztian Buza
    • 1
  • Lars Schmidt-Thieme
    • 1
  1. 1.Information Systems and Machine Learning Lab (ISMLL)University of HildesheimHildesheimGermany

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