TERMINOLOGICAL KNOWLEDGE STRUCTURE FOR INTERMEDIARY EXPERT-SYSTEMS

An intermediary expert system (IES) helps both end users and professio nal searchers to conduct their online database searching. To provide a dvice about term selection and query expansion, an IES should include a terminological knowledge structure. Terminological attributes as wel l as other properties could provide the starting point for building a knowledge base, and knowledge acquisition could rely on knowledge-base techniques coupled with statistical techniques. The searching behavio r of expert online searchers would provide one source of knowledge. Th e knowledge structure would include three constructs or each term: fre quency data, a hedge, and a position in a classification scheme. Switc hing vocabularies or languages could provide a meta-schema and facilit ate the interoperability of databases in similar subject domains. To d evelop such knowledge structure, future research should focus on termi nological attributes, word and phrase disambiguation, automated text p rocessing, and the role of thesauri and classification schemes in inde xing and retrieval. In particular, such research should develop techni ques that combine knowledge-base and statistical methods and that cons ider user preferences.