Extended Document Representation for Search Result Clustering

  • S. Hoa Nguyen
  • Wojciech Świeboda
  • Grzegorz Jaśkiewicz
Part of the Studies in Computational Intelligence book series (SCI, volume 390)


Organizing query results into clusters facilitates quick navigation through search results and helps users to specify their search intentions. Most meta-search engines group documents based on short fragments of source text called snippets. Such a model of data representation in many cases shows to be insufficient to reflect semantic correlation between documents. In this paper, we discuss a framework of document description extension which utilizes domain knowledge and semantic similarity. Our idea is based on application of Tolerance Rough Set Model, semantic information extracted from source text and domain ontology to approximate concepts associated with documents and to enrich the vector representation.


Text mining semantic clustering DBpedia document grouping PubMed bibliometric measure 


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • S. Hoa Nguyen
    • 1
  • Wojciech Świeboda
    • 1
  • Grzegorz Jaśkiewicz
    • 1
  1. 1.Faculty of Mathematics, Informatics, and MechanicsThe University of WarsawWarsawPoland

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