  Cross-lingual entity alignment (EA) aims to find the equivalent entities
between crosslingual KGs, which is a crucial step for integrating KGs.
Recently, many GNN-based EA methods are proposed and show decent performance
improvements on several public datasets. Meanwhile, existing GNN-based EA
methods inevitably inherit poor interpretability and low efficiency from neural
networks. Motivated by the isomorphic assumption of GNNbased methods, we
successfully transform the cross-lingual EA problem into the assignment
problem. Based on this finding, we propose a frustratingly Simple but Effective
Unsupervised entity alignment method (SEU) without neural networks. Extensive
experiments show that our proposed unsupervised method even beats advanced
supervised methods across all public datasets and has high efficiency,
interpretability, and stability.

Computation and Language;Artificial Intelligence