The problem-solving strategy applied in knowledge-based systems may often be characterized as classification. Central to classification is computation of the degree to which an object is an instance of a given class (concept, category). Two kinds of problems, namely object-querying and class-querying, as exemplified by, respectively, information retrieval systems and expert systems, are distinguished. In the first kind, the problem is to identify the objects (e.g. documents) to which a given concept (the query) applies. In the second kind, the problem is to identify the concepts (categories) that apply to a given object (the observation). A fuzzy-set-based scheme for construction of efficient problem solving systems of the two kinds is developed. The problem of vocabulary mismatch in information retrieval is considered, and the scheme is proposed as a solution to this problem. The knowledge base applies a term-centered representation form called a fuzzy relational thesaurus. To avoid recomputation of deductive information in problem-solving tasks, the deductive closure of the knowledge base is derived at the outset. This closure is computed in O(n/sup 3/) time.
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