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.
翻译:在制定基于案例的理由说明(CBR)系统的早期阶段,确定相似性措施的定义具有挑战性,因为这项任务要求将领域专家的隐性知识转移到知识表述中,虽然整个CBR系统的解释性很强,但相似性措施决定了排名,但不一定表明哪些特征有助于高(或低)排名。在本文件中,我们介绍了我们为类似性模拟开放知识工程进程的工作。这项工作是大赦国际和公共卫生研究人员开展跨学科研究协作的结果,开发电子保健应用。在这一过程中,对发展进程的解释性和透明度至关重要,以便让领域专家对开发过程进行深入的质量保证。