This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations. We consider three main perspectives in which ontologies can contribute significantly, namely reference modelling, common-sense reasoning, and knowledge refinement and complexity management. We overview some of the existing approaches in the literature, and we position them according to these three proposed perspectives. The paper concludes by discussing what challenges still need to be addressed to enable ontology-based approaches to explanation and to evaluate their human-understandability and effectiveness.

翻译：本文探讨了显式知识（特别是本体论）在可解释人工智能及以人为中心的可解释系统与可理解解释开发中所能发挥的不同作用。我们从三个主要视角审视本体论的重要贡献：参考建模、常识推理，以及知识精炼与复杂性管理。通过梳理文献中的现有方法，我们将其归类于上述三个视角。最后，本文讨论了仍需解决哪些挑战，以推动基于本体论的解释方法的发展，并评估其人类可理解性与有效性。