Exploiting Transitivity Constraints for Entity Matching in Knowledge Graphs
This addresses structural consistency issues in knowledge graph integration, but is incremental as it builds on existing similarity measures and cluster editing techniques.
The paper tackled the problem of entity matching in knowledge graphs, where identified entity pairs often violate transitivity, and showed that simply taking the transitive closure reduces precision. They proposed a method using cluster editing to enforce transitivity with minimal spurious links, leading to improved performance.
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.