Explanation Service for Complex CBR Applications

Case-based Reasoning (CBR) is a mature technology for building knowledge-based systems that are capable to produce useful results even if no answer matches the query exactly. Often the result sets presented to users are ordered by means of similarity and utility. However, for complex applications with knowledge intensive domains we have discovered that results sets enriched by calculated similarity values for particular answers are not sufficient. Users have a demand for additional information and explanations making the proposed results more transparent. By presenting additional explanations to them, their confidence in the result set increases and possible deficiencies, e.g., in the weight model, can be revealed and corrected. This paper presents a realized explanation service that combines several existing and new explanation technologies into one system.