Case-based Reasoning (CBR) is a mature technology for building knowledge-based systems. Unlike with reasoning approaches making use of deductive inference, CBR-based applications are capable to produce useful results even if no answer matches the query exactly. Result sets presented to users are ordered by means of similarity and utility. However, for knowledge intensive domains we discovered that results sets enriched by calculated similarity values for particular answers are not sufficient for experts. Such 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. In this position paper we investigate explanation approaches for CBR from the user level perspective. Besides identifying potential uses cases, we sketch techniques for creating different kinds of explanations and relate them to already existing approaches from other areas of CBR research, e.g. conversational CBR.
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