On the Multiple Roles of Ontologies in Explainable AI
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 19 of its 72 references that are in the map. First posted on arXiv 8 Nov 2023.
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19 of 72 references.
References · 19
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7 more
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