Ontologies formalise how the concepts from a given domain are interrelated. Despite their clear potential as a backbone for explainable AI, existing ontologies tend to be highly incomplete, which acts as a significant barrier to their more widespread adoption. To mitigate this issue, we present a mechanism to infer plausible missing knowledge, which relies on reasoning by analogy. To the best of our knowledge, this is the first paper that studies analogical reasoning within the setting of description logic ontologies. After showing that the standard formalisation of analogical proportion has important limitations in this setting, we introduce an alternative semantics based on bijective mappings between sets of features. We then analyse the properties of analogies under the proposed semantics, and show among others how it enables two plausible inference patterns: rule translation and rule extrapolation.

翻译：肿瘤学将特定领域的概念相互关联的形式化。 尽管现有的肿瘤学显然具有作为可解释的AI的支柱的明显潜力,但现有的肿瘤学往往是高度不完整的,是阻碍其被广泛采用的重大障碍。为了缓解这一问题,我们提出了一个机制来推断可信的缺失知识,而这种缺失知识是通过类推推推出来的。据我们所知,这是第一份文件,在描述逻辑学的逻辑设置中研究类推推。在表明模拟比例的标准正规化在这一背景下有重大局限性之后,我们采用了基于各组地物之间两面图的替代语义学。我们随后分析了拟议语义学下相似物的特性,并展示了它如何促成两种可信的推论模式:规则翻译和规则外推法。