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How to identify those equivalent entities between knowledge graphs (KGs),which is called Entity Alignment (EA), is a long-standing challenge. So far,many methods have been proposed, with recent focus on leveraging Deep Learningto solve this problem. However, we observe that most of the efforts has beenpaid to having better representation of entities, rather than improving entitymatching from the learned representations. In fact, how to efficiently inferthe entity pairs from this similarity matrix, which is essentially a matchingproblem, has been largely ignored by the community. Motivated by thisobservation, we conduct an in-depth analysis on existing algorithms that areparticularly designed for solving this matching problem, and propose a novelmatching method, named Bidirectional Matching (BMat). Our extensiveexperimental results on public datasets indicate that there is currently nosingle silver bullet solution for EA. In other words, different classes ofentity similarity estimation may require different matching algorithms to reachthe best EA results for each class. We finally conclude that using PARIS, thestate-of-the-art EA approach, with BMat gives the best combination in terms ofEA performance and the algorithm's time and space complexity.

A Benchmarking Study of Matching Algorithms for Knowledge Graph Entity Alignment