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.
@article{arxiv.2104.12589,
title = {Exploiting Transitivity Constraints for Entity Matching in Knowledge Graphs},
author = {Jurian Baas and Mehdi Dastani and Ad Feelders},
journal= {arXiv preprint arXiv:2104.12589},
year = {2021}
}