On the multiple roles of ontologies in explanations for neuro-symbolic AI

Abstract

There has been a renewed interest in symbolic AI in recent years. Symbolic AI is indeed one of the key enabling technologies for the development of neuro-symbolic AI systems, as it can mitigate the limited capabilities of black box deep learning models to perform reasoning and provide support for explanations. This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in drawing intelligible explanations in neuro-symbolic AI. 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 some open challenges related to the adoption of ontologies in explanations.