  During the early stages of developing Case-Based Reasoning (CBR) systems the
definition of similarity measures is challenging since this task requires
transferring 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 present our work on
opening the knowledge engineering process for similarity modelling. This work
present is a result of an interdisciplinary research collaboration between AI
and public health 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.
