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

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

Research output: Contribution to journalArticle

25 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)206-216
Number of pages11
JournalJournal of the American Society for Information Science
Issue number3
StatePublished - Dec 1 1998


ASJC Scopus subject areas

  • Engineering(all)

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