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.

整合归纳推理和演绎推理的过程，系统文献综述发现研究了机器学习和本体结合的文章，并将归纳推理（由机器学习执行）和演绎推理（由本体执行）结合到人工智能系统中。通过分析 128 项研究，我们找到了三个主要的机器学习和本体结合的类别：增强本体、语义数据挖掘、学习与推理系统，并强调了这些研究中使用的多种机器学习算法。此外，我们还将我们的分类与领域内类似的近期工作和神经符号方法进行了比较。