  Motivated by the desire to explore the process of combining inductive and
deductive reasoning, we conducted a systematic literature review of articles
that investigate the integration of machine learning and ontologies. The
objective was to identify diverse techniques that incorporate both inductive
reasoning (performed by machine learning) and deductive reasoning (performed by
ontologies) into artificial intelligence systems. Our review, which included
the analysis of 128 studies, allowed us to identify three main categories of
hybridization between machine learning and ontologies: learning-enhanced
ontologies, semantic data mining, and learning and reasoning systems. We
provide a comprehensive examination of all these categories, emphasizing the
various machine learning algorithms utilized in the studies. Furthermore, we
compared our classification with similar recent work in the field of hybrid AI
and neuro-symbolic approaches.
