Artificial Intelligence Lab, Department of MIS, University of Arizona / 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 â â varietyâ â in search terms and thereby reduce search uncertainty.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/106252 |
| Date | January 1998 |
| Creators | Chen, Hsinchun, Martinez, Joanne, Kirchhoff, Amy, Ng, Tobun Dorbin, Schatz, Bruce R. |
| Publisher | Wiley Periodicals, Inc |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
Page generated in 0.002 seconds