On the Multiple Roles of Ontologies in Explainable AI
It addresses the need for more interpretable AI systems, but is incremental as it reviews and categorizes existing approaches without introducing new methods or results.
The paper examines how ontologies can enhance Explainable AI by serving as reference models, enabling common-sense reasoning, and managing knowledge complexity, while identifying challenges in evaluating their human-understandability and effectiveness.
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.