Similarities between entities occur frequently in many real-world scenarios. For over a century, researchers in different fields have proposed a range of approaches to measure the similarity between entities. More recently, inspired by "Google Sets", significant academic and commercial efforts have been devoted to expanding a given set of entities with similar ones. As a result, existing approaches nowadays are able to take into account properties shared by entities, hereinafter called nexus of similarity. Accordingly, machines are largely able to deal with both similarity measures and set expansions. To the best of our knowledge, however, there is no way to characterize nexus of similarity between entities, namely identifying such nexus in a formal and comprehensive way so that they are both machine- and human-readable; moreover, there is a lack of consensus on evaluating existing approaches for weakly similar entities. As a first step towards filling these gaps, we aim to complement existing literature by developing a novel logic-based framework to formally and automatically characterize nexus of similarity between tuples of entities within a knowledge base. Furthermore, we analyze computational complexity aspects of this framework.

本研究旨在开发一种基于逻辑的框架来形式化和自动化地描述知识库中实体之间的相似性，同时分析此框架的计算复杂性方面。

基于逻辑的知识库相似性关系探究：框架与计算复杂度分析