Automatic inference of indexing rules for MEDLINE

BMC Bioinformatics. 2008 Nov 19;9 Suppl 11(Suppl 11):S11. doi: 10.1186/1471-2105-9-S11-S11.


Background: Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE.

Methods: In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers.

Results: Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE.

Conclusion: We expect the sets of ILP rules obtained in this experiment to be integrated into MTI.

Publication types

  • Research Support, N.I.H., Intramural

MeSH terms

  • Abstracting and Indexing / methods*
  • Algorithms
  • Artificial Intelligence
  • Medical Subject Headings*
  • Natural Language Processing*
  • Programming Languages