  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.
