Inconsistency handling is an important issue in knowledge management. Especially in ontology engineering, logical inconsistencies may occur during ontology construction. A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology. However, previous studies on selecting maximum consistent subsets have rarely considered the semantics of the axioms, which may result in irrational inference. In this paper, we propose a novel approach to reasoning with inconsistent ontologies in description logics based on the embeddings of axioms. We first give a method for turning axioms into distributed semantic vectors to compute the semantic connections between the axioms. We then define an embedding-based method for selecting the maximum consistent subsets and use it to define an inconsistency-tolerant inference relation. We show the rationality of our inference relation by considering some logical properties. Finally, we conduct experiments on several ontologies to evaluate the reasoning power of our inference relation. The experimental results show that our embedding-based method can outperform existing inconsistency-tolerant reasoning methods based on maximal consistent subsets.

本文提出了在基于描述逻辑的不一致本体中利用嵌入学习方法进行推理的新方法，并使用语义向量来计算公理之间的语义连接，从而定义了一种基于嵌入的方法来选择最大一致子集，并用它来定义一种不一致容忍的推理关系，并且实验结果表明，我们的嵌入式方法可以胜过现有的基于最大一致子集的不一致容忍推理方法。

一种基于嵌入的方法，用于处理不一致本体的不一致容忍推理