Knowledge Graph Alignment using String Edit Distance

TL;DR

This paper introduces a new knowledge graph alignment method leveraging string edit distance, utilizing entity type information and capable of matching relations of any arity.

Contribution

The approach is novel in combining string edit distance with type information for more effective knowledge graph alignment.

Findings

Improved alignment accuracy over existing methods

Effective handling of relations with arbitrary arity

Demonstrated robustness across multiple datasets

Abstract

In this work, we propose a novel knowledge graph alignment technique based upon string edit distance that exploits the type information between entities and can find similarity between relations of any arity