On the Explanation of Similarity for Developing and Deploying CBR Systems

TL;DR

This paper explores methods to improve the explainability of similarity measures in CBR systems, emphasizing transparency in the knowledge engineering process to enhance quality assurance in e-Health applications.

Contribution

It introduces an approach to open the knowledge engineering process for similarity modeling, integrating explainability and transparency in CBR system development.

Findings

Enhanced explainability of similarity measures

Improved transparency in knowledge engineering

Facilitated quality assurance by domain experts

Abstract

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.