Exploiting a Thesaurus-Based Semantic Net for Knowledge-Based Search

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With the growth of on-line information, the need for better resource location services is growing rapidly. A popular goal is to conduct search in terms of concepts, rather than words; however, this approach is frequently thwarted by the high up-front cost of building an adequate ontology (conceptual vocabulary) in the first place. In this paper we describe a knowledge-based Expert Locator application (for identifying human experts relevant to a particular problem or interest), which addresses this issue by using a large, pre-built, technical thesaurus as an initial ontology, combined with simple AI techniques of search, subsumption computation, and lan-guage processing. The application has been deployed and in use in our local organization since June, 1999, and a second, larger application was deployed in March 2000. We present the Expert Locator and the AI techniques it uses, and then we evaluate and discuss the application. The significance of this work is that it demonstrates how years of work by library science in thesaurus-building can be leveraged using AI methods, to construct a practical resource location service in a short period of time.