From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment

Xin Mao; Wenting Wang; Yuanbin Wu; Man Lan

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arXiv · 2109.02363

From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment

Xin Mao·Wenting Wang·Yuanbin Wu·Man Lan

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Abstract

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.

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Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan. 2021-09-15. From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment. https://arxiv.org/abs/2109.02363

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