Improving Information Retrieval in MEDLINE by Modulating MeSH Term Weights

  • Kwangcheol Shin
  • Sang-Yong Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3136)


MEDLINE is a widely used very large database of natural language medical data, mainly abstracts of research papers in medical domain. The documents in it are manually supplied with keywords from a controlled vocabulary, called MeSH terms. We show that (1) a vector space model-based retrieval system applied to the full text of the documents gives much better results than the Boolean model-based system supplied with MEDLINE, and (2) assigning greater weights to the MeSH terms than to the terms in the text of the documents provides even better results than the standard vector space model. The resulting system outperforms the retrieval system supplied with MEDLINE as much as 2.4 times.


Cystic Fibrosis MeSH Term Vector Space Model Boolean Model Document Vector 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dhillon, I.S., Modha, D.S.: Concept Decomposition for Large Sparse Text Data using Clustering. Technical Report RJ 10147(9502), IBM Almaden Research Center (1999)Google Scholar
  2. 2.
    Dhillon, I.S., Fan, J., Guan, Y.: Efficient Clustering of Very Large Document Collections. In: Data Mining for Scientific and Engineering Applications, Kluwer, Dordrecht (2001)Google Scholar
  3. 3.
    Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)Google Scholar
  4. 4.
    Frakes, W.B., Baeza-Yates, R.: Information Retrieval: Data Structures and Algorithms. Prentince Hall, Englewood Cliffs (1992)Google Scholar
  5. 5.
    Gelbukh, A.: Lazy Query Enrichment: A Simple Method of Indexing Large Specialized Document Bases. In: Ibrahim, M., Küng, J., Revell, N. (eds.) DEXA 2000. LNCS, vol. 1873, pp. 526–535. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Gelbukh, A., Sidorov, G., Guzmán-Arenas, A.: A Method of Describing Document Contents through Topic Selection. In: Proc. SPIRE 1999, String Processing and Information Retrieval, pp. 73–80. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  7. 7.
    Ide, E.: New experiments in relevance feedback. In: Salton, G. (ed.) The SMART Retrieval System, pp. 337–354. Prentice-Hall, Englewood Cliffs (1971)Google Scholar
  8. 8.
    Lowe, H.J., Barnett, O.: Understanding and using the medical subject headings (MeSH) vocabulary to perform literature searches. J. American Medical Association 273(184) (1995)Google Scholar
  9. 9.
    Montes-y-Gomez, M., López López, A., Gelbukh, A.: Document Title Patterns in Information Retrieval. In: Matoušek, V., Mautner, P., Ocelíková, J., Sojka, P. (eds.) TSD 1999. LNCS (LNAI), vol. 1692, pp. 364–367. Springer, Heidelberg (1999)Google Scholar
  10. 10.
    Montes-y-Gomez, M., López López, A., Gelbukh, A.: Information Retrieval with Conceptual Graph Matching. In: Ibrahim, M., Küng, J., Revell, N. (eds.) DEXA 2000. LNCS, vol. 1873, pp. 312–321. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Porter, M.: An algorithm for suffix stripping. Program 14, 130–137 (1980)Google Scholar
  13. 13.
    Salton, G., McGill, M.J.: Introduction to Modern Retrieval. McGraw-Hill, New York (1983)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kwangcheol Shin
    • 1
  • Sang-Yong Han
    • 1
  1. 1.Computer Science and Engineering DepartmentChung-Ang UniversitySeoulKorea

Personalised recommendations