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.

中文翻译：

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开发和部署社区康复系统的相似性解释

在开发基于案例的推理 (CBR) 系统的早期阶段，相似性度量的定义具有挑战性，因为该任务需要将领域专家的隐式知识转换为知识表示。虽然整个 CBR 系统非常具有解释性，但相似性度量决定了排名，但不一定显示哪些特征有助于高（或低）排名。在本文中，我们介绍了我们在为相似性建模打开知识工程过程方面的工作。目前的这项工作是人工智能和公共卫生研究人员开发电子健康应用程序之间跨学科研究合作的结果。在这项工作中，开发过程的可解释性和透明度对于允许领域专家进行深入的质量保证至关重要。