Ranking indirect connections in literature-based discovery: The role of medical subject headings

Don R. Swanson, Neil R. Smalheiser, Vetle I. Torvik

Research output: Contribution to journalArticle

Abstract

Arrowsmith, a computer-assisted process for literature-based discovery, takes as input two disjoint sets of records (A, C) from the Medline database. It produces a list of title words and phrases, B, that are common to A and C, and displays the title context In which each B-term occurs within A and within C. Subject experts then can try to find A-B and B-C title-pairs that together may suggest novel and plausible indirect A-C relationships (via B-terms) that are of particular interest in the absence of any known direct A-C relationship. The list of B-terms typically is so large that it is difficult to find the relatively few that contribute to scientifically interesting connections. The purpose of the present article is to propose and test several techniques for improving the quality of the B-list. These techniques exploit the Medical Subject Headings (MeSH) that are assigned to each input record. A MesH-based concept of literature cohesiveness is defined and plays a key role. The proposed techniques are tested on a published example of indirect connections between migraine and magnesium deficiency. The tests demonstrate how the earlier results can be replicated with a more efficient and more system-atic computer-aided process.

Original languageEnglish (US)
Pages (from-to)1427-1439
Number of pages13
JournalJournal of the American Society for Information Science and Technology
Volume57
Issue number11
DOIs
StatePublished - Sep 1 2006
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence

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