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Alleviating search uncertainty through concept associations: Automatic indexing, co‐occurrence analysis, and parallel computing

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  • Hsinchun Chen
  • Joanne Martinez
  • Amy Kirchhoff
  • Tobun D. Ng
  • Bruce R. Schatz

Abstract

In this article, we report research on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co‐occurrence analysis, we performed a large‐scale experiment using a parallel supercomputer (SGI Power Challenge) to analyze 400,000+ abstracts in an INSPEC computer engineering collection. Two system‐generated thesauri, one based on a combined object filtering and automatic indexing method, and the other based on automatic indexing only, were compared with the human‐generated INSPEC subject thesaurus. Our user evaluation revealed that the system‐generated thesauri were better than the INSPEC thesaurus in concept recall, but in concept precision the 3 thesauri were comparable. Our analysis also revealed that the terms suggested by the 3 thesauri were complementary and could be used to significantly increase “variety” in search terms and thereby reduce search uncertainty. © 1998 John Wiley & Sons, Inc.

Suggested Citation

  • Hsinchun Chen & Joanne Martinez & Amy Kirchhoff & Tobun D. Ng & Bruce R. Schatz, 1998. "Alleviating search uncertainty through concept associations: Automatic indexing, co‐occurrence analysis, and parallel computing," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(3), pages 206-216, March.
    • Handle: RePEc:bla:jamest:v:49:y:1998:i:3:p:206-216
    DOI: 10.1002/(SICI)1097-4571(199803)49:33.0.CO;2-K
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    Cited by:

    1. Ed Noyons, 2001. "Bibliometric mapping of science in a policy context," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(1), pages 83-98, January.

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