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.

本文讨论明确的知识，特别是本体论在可解释的人工智能和人类中心的可解释系统及可理解解释的发展中所起的不同角色。我们考虑到本体论在参考建模、常识推理和知识完善与复杂性管理方面能够做出的显著贡献，并概述了一些现有的方法，将它们与这三个提出的视角进行了定位。最后，本文讨论了仍需解决的挑战，以实现基于本体论的解释方法，并评估其人类理解能力和有效性。