Paradox in Applications of Semantic Similarity Models in Information Retrieval

  • Hai Dong
  • Farookh Khadeer Hussain
  • Elizabeth Chang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 11)


Semantic similarity models are a series of mathematical models for computing semantic similarity values among nodes in a semantic net. In this paper we reveal the paradox in the applications of these semantic similarity models in the field of information retrieval, which is that these models rely on a common prerequisite – the words of a user query must correspond to the nodes of a semantic net. In certain situations, this sort of correspondence can not be carried out, which invalidates the further working of these semantic similarity models. By means of two case studies, we analyze these issues. In addition, we discuss some possible solutions in order to address these issues. Conclusion and future works are drawn in the final section.


information retrieval semantic net semantic similarity models 


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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2009

Authors and Affiliations

  • Hai Dong
    • 1
  • Farookh Khadeer Hussain
    • 1
  • Elizabeth Chang
    • 1
  1. 1.Digital Ecosystems and Business Intelligence InstituteCurtin University of TechnologyPerthAustralia

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