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

Research is reported on an algorithmic approach to alleviating search uncertainty in a large information space. Grounded on object filtering, automatic indexing, and co-occurrence analysis, a large-scale experiment was performed 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. A 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. The 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.