  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.
