Fuzzy Rating Framework for Knowledge Management

Abstract In this work we deploy five phases of the Case-based Reasoning in a fuzzy rating framework, which illustrates a holistic knowledge management method including eliciting expert's experience into case base, validating the expert's expertise in knowledge-consistency level, aggregating the judgments of weighted experts into final ratings, solving new problem by retrieving the relevant cases, and retaining the adapted case into case base once the experience had been learned. The main contribution of this framework is to explore a novel measure of knowledge-consistency level (KC ratio) for identifying experts in performance-rating domain. The would-be experts' own judgments were used to validate their knowledge qualities. Moreover, the framework results in an optimal consensus for the performance rating with the help of experts