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

Characterizing Nexus of Similarity within Knowledge Bases: A Logic-based Framework and its Computational Complexity Aspects