Abstract
This paper addresses the problem of verification of knowledge bases. It presents a knowledge-based system (KBS) verification approach that considers system specifications and, consequently, knowledge bases to be partially described when development starts. This partial description is not necessarily perfect, and our work aims at using machine learning techniques to progressively acquire new knowledge and then improve the quality of expert system knowledge bases (KB) by coping with two major KB anomalies: incompleteness and incorrectness. The KBs considered in our approach are expressed in different formalisms.
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