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.Comment: 11 pages, 1 figure, 7 table