Advanced Relevance Feedback Query Expansion Strategy for Information Retrieval in MEDLINE

  • Kwangcheol Shin
  • Sang-Yong Han
  • Alexander Gelbukh
  • Jaehwa Park
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3287)


MEDLINE is a very large database of abstracts of research papers in medical domain, maintained by the National Library of Medicine. Documents in MEDLINE are supplied with manually assigned keywords from a controlled vocabulary called MeSH terms, classified for each document into major MeSH terms describing the main topics of the document and minor MeSH terms giving more details on the document’s topic. To search MEDLINE, we apply a query expansion strategy through automatic relevance feedback, with the following modification: we assign greater weights to the MeSH terms, with different modulation of the major and minor MeSH terms’ weights. With this, we obtain 16% of improvement of the retrieval quality over the best known system.


Cystic Fibrosis Information Retrieval MeSH Term Relevance Feedback Query Expansion 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Kwangcheol Shin
    • 1
  • Sang-Yong Han
    • 1
  • Alexander Gelbukh
    • 1
    • 2
  • Jaehwa Park
    • 1
  1. 1.School of Computer Science and EngineeringChung-Ang UniversitySeoulKorea
  2. 2.Center for Computing ResearchNational Polytechnic InstituteZacatenco

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