On the Explanation of Similarity for Developing and Deploying CBR Systems

During the early stages of developing Case-Based Reasoning (CBR) systems the definition of similarity measures is challenging since this task requires to transfer implicit knowledge of domain experts into knowledge representations. While an entire CBR system is very explanatory, the similarity measure determines the ranking but do not necessarily show which features contribute to high (or low) rankings. In this paper we will present our work on opening the knowledge engineering process for similarity modelling. We will present how we transfer implicit knowledge from experts as well as a data-driven approach into case and similarity representations for CBR systems. The work present is a result of interdisciplinary research collaborations between AI and medical researchers developing e-Health applications. During this work, explainability and transparency of the development process is crucial to allow in-depth quality assurance of the by the domain experts.