A Benchmarking Study of Matching Algorithms for Knowledge Graph Entity Alignment

TL;DR

This paper evaluates various entity matching algorithms for knowledge graph entity alignment, introduces a new method called Bidirectional Matching (BMat), and finds that combining BMat with PARIS yields optimal results.

Contribution

The paper provides an in-depth analysis of matching algorithms for entity alignment, introduces BMat, and demonstrates the effectiveness of combining BMat with PARIS.

Findings

No single matching algorithm is best for all cases.

Different similarity estimation methods require different matching algorithms.

BMat combined with PARIS achieves the best overall performance.

Abstract

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 Learning to solve this problem. However, we observe that most of the efforts has been paid to having better representation of entities, rather than improving entity matching from the learned representations. In fact, how to efficiently infer the entity pairs from this similarity matrix, which is essentially a matching problem, has been largely ignored by the community. Motivated by this observation, we conduct an in-depth analysis on existing algorithms that are particularly designed for solving this matching problem, and propose a novel matching method, named Bidirectional Matching (BMat). Our extensive experimental results on public datasets indicate that there is currently no single silver bullet solution for EA. In other words, different classes of entity similarity estimation may require different matching algorithms to reach the best EA results for each class. We finally conclude that using PARIS, the state-of-the-art EA approach, with BMat gives the best combination in terms of EA performance and the algorithm's time and space complexity.