Integrating MeSH Ontology to Improve Medical Information Retrieval

  • M. C. Díaz-Galiano
  • M. Á. García-Cumbreras
  • M. T. Martín-Valdivia
  • A. Montejo-Ráez
  • L. A. Ureña-López
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5152)

Abstract

This paper describes the SINAI team participation in the ImageCLEFmed campaign. The SINAI research group has participated in the multilingual image retrieval subtask. The experiments accomplished are based on the integration of specific knowledge in the topics.

We have used the MeSH ontology to expand the queries. The expansion consists in searching terms from the topic query in the MeSH ontology in order to add similar terms. We have processed the set of collections using Information Gain (IG) in the same way as in ImageCLEFmed 2006.

In our experiments mixing visual and textual information we obtain better results than using only textual information. The weigth of the textual information is very strong in this mixed strategy. In the experiments with a low textual weight, the use of IG improves the results obtained.

Keywords

Entropy Stopper MeSH 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • M. C. Díaz-Galiano
    • 1
  • M. Á. García-Cumbreras
    • 1
  • M. T. Martín-Valdivia
    • 1
  • A. Montejo-Ráez
    • 1
  • L. A. Ureña-López
    • 1
  1. 1.SINAI Research Group, Computer Science DepartmentUniversity of JaénSpain

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