F_MP: A Fuzzy Match Framework for Rule-Based Programming

Rule-based expert system shells have one important drawback in handling uncertain knowledge. It is the drawback that the style of their fuzzy reasoning process and their semantics both are not compatible with those of relational databases. On the other hand, production rule-based languages whose structure is similar to that of the databases fail to possess the fuzzy reasoning ability. Proposed in this paper is a framework to support a semantic based inexact match with Fuzzy Match Predicate (F MP). In a uniform way it allows matches including fuzzy linguistic variables as well as fuzzy numbers. Our framework also adopts a design alternative to conform not only the semantics of its knowledge representation but also its reasoning style to those of the relational framework. It is a natural consequence that such a design alternative entails a seamless integration of our work into the relational databases. Major advantage of our framework is that it can be implemented on top of the production rule-based languages without modifying their discrimination networks. That is mainly due to the minimal semantic gap between the framework and the languages. In this paper, we demonstrate that 1) F MP is a uniform framework to provide the rule-based languages with fuzzy match facilities semantically enhanced and that 2) its semantic conforms well to that of the relational one. We also develop a rule evaluation mechanism well suited to the aims.