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.

翻译：跨语言实体协调(EA)旨在找到跨语言KG之间的等同实体,这是整合KG的关键步骤。 最近,提出了许多基于GNN的EA方法,在几个公共数据集中表现出了体面的业绩改进。与此同时,基于GNN的现有EA方法不可避免地会继承神经网络的不良可解释性和低效率。受基于GNN方法的无定型假设的驱动,我们成功地将跨语言的EA问题转化为分配问题。基于这一结论,我们提出了一种令人沮丧的简单但有效且不受监督的实体协调方法(SEU),但没有神经网络。广泛的实验表明,我们提议的不受监督的方法甚至在所有公共数据集中击败了先进的监督方法,并且具有很高的效率、可解释性和稳定性。