  The goal of entity matching in knowledge graphs is to identify entities that
refer to the same real-world objects using some similarity metric. The result
of entity matching can be seen as a set of entity pairs interpreted as the
same-as relation. However, the identified set of pairs may fail to satisfy some
structural properties, in particular transitivity, that are expected from the
same-as relation. In this work, we show that an ad-hoc enforcement of
transitivity, i.e. taking the transitive closure, on the identified set of
entity pairs may decrease precision dramatically. We therefore propose a
methodology that starts with a given similarity measure, generates a set of
entity pairs that are identified as referring to the same real-world objects,
and applies the cluster editing algorithm to enforce transitivity without
adding many spurious links, leading to overall improved performance.
