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.

翻译：如何识别知识图谱间的等价实体（即实体对齐）是一个长期存在的挑战。迄今为止，已有许多方法被提出，近期研究重点在于利用深度学习解决该问题。然而，我们发现大多数工作集中于改进实体表征，而非基于所学表征提升实体匹配效果。实际上，如何从相似度矩阵中高效推断实体对，这一本质上的匹配问题，已被学界长期忽视。基于这一发现，我们对专用于解决该匹配问题的现有算法进行了深度分析，并提出了一种新型匹配方法——双向匹配（BMat）。在公开数据集上的大量实验结果表明，目前尚不存在适用于实体对齐的万能解决方案。换言之，不同类别的实体相似度估计方法可能需要不同的匹配算法才能达到最优结果。我们最终得出结论：采用当前最优实体对齐方法PARIS配合BMat，能在实体对齐性能与算法时空复杂度之间达成最佳平衡。