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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)

Abstract

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.

Keywords

information retrieval semantic net semantic similarity models 

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References

  1. 1.
    Andreopoulos, B., Alexopoulou, D., Schroeder, M.: Word Sense Disambiguation in Biomedical Ontologies with Term Co-occurrence Analysis and Document Clustering. Int. J. Data Mining and Bioinformatics 2, 193–215 (2008)CrossRefGoogle Scholar
  2. 2.
    Jiang, J.J., Conrath, D.W.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In: International Conference on Research in Computational Linguistics (ROCLING X), Taiwan, pp. 19–33 (1997)Google Scholar
  3. 3.
    Curtis, J.C., Baxter, D.: On the Application of the Cyc Ontology to Word Sense Disambiguation. In: The 19th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2006). AAAI Press, Melbourne Beach (2006)Google Scholar
  4. 4.
    Joshi, M., Pedersen, T., Maclin, R., Pakhomov, S.: Kernel Methods for Word Sense Disambiguation and Acronym Expansion. In: The 21st National Conference on Artificial Intelligence (AAAI 2006). AAAI, Boston (2006)Google Scholar
  5. 5.
    Leacock, C., Chodorow, M.: Combining Local Context and WordNet Similarity for Word Sense Identification. In: WordNet: An Electronic Lexical Database, pp. 265–283. MIT Press, Cambridge (1998)Google Scholar
  6. 6.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and Application of a Metric on semantic nets. IEEE Transactions on Systems, Man and Cybernetics 19, 17–30 (1989)CrossRefGoogle Scholar
  7. 7.
    Resnik, P.: Semantic Similarity in A Taxonomy: An Information-based Measure and Its Application to Problems of Ambiguity in Natural Language. Journal of Artificial Intelligence Research 11, 95–130 (1999)zbMATHGoogle Scholar
  8. 8.
    Sowa, J.F.: Semantic Networks. In: Shapiro, S.C. (ed.) Encyclopedia of Artificial Intelligence. Wiley, Chichester (1992)Google Scholar

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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