Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph. In this paper, we show that it can also be used to explain data differentials, for example in the context of Explainable AI (XAI), and we show that it can in fact be done in a way that is meaningful to a human observer. Our approach utilizes a large class hierarchy, curated from the Wikipedia category hierarchy, as background knowledge. To make the explanations easily understandable for non-specialists, the complex description logic explanations generated by our concept induction system (ECII) were presented as a word list consisting of the concept names occurring in the highest rated system responses.

概念归纳基于描述逻辑的形式逻辑推理，已用于本体工程，以便从基础数据 (ABox) 图创建本体 (TBox) 公理。在本文中，我们表明它也可以用于解释数据差异，例如在可解释人工智能（XAI）的背景下，并且我们表明它实际上可以以对人类观察者有意义的方式来完成。我们的方法利用从维基百科类别层次结构中整理的大型类层次结构作为背景知识。为了使非专业人士能够轻松理解解释，我们的概念生成了复杂的描述逻辑解释归纳系统（ECII）以单词列表的形式呈现，其中包含出现在评分最高的系统响应中的概念名称。