From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment

EMNLP 2021  ·  Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan ·

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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Datasets


Results from the Paper


Ranked #4 on Entity Alignment on dbp15k fr-en (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Entity Alignment dbp15k fr-en SEU Hits@1 0.988 # 4
Entity Alignment dbp15k ja-en SEU Hits@1 0.956 # 5
Entity Alignment DBP15k zh-en SEU Hits@1 0.900 # 5

Methods


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