From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment

Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan


Abstract
Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs (Knowledge Graphs), 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. However, existing GNN-based EA methods inevitably inherit poor interpretability and low efficiency from neural networks. Motivated by the isomorphic assumption of GNN-based methods, we successfully transform the cross-lingual EA problem into an assignment problem. Based on this re-definition, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. Extensive experiments have been conducted to show that our proposed unsupervised approach even beats advanced supervised methods across all public datasets while having high efficiency, interpretability, and stability.
Anthology ID:
2021.emnlp-main.226
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2843–2853
Language:
URL:
https://aclanthology.org/2021.emnlp-main.226
DOI:
10.18653/v1/2021.emnlp-main.226
Bibkey:
Cite (ACL):
Xin Mao, Wenting Wang, Yuanbin Wu, and Man Lan. 2021. From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2843–2853, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment (Mao et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.226.pdf
Software:
 2021.emnlp-main.226.Software.zip
Video:
 https://aclanthology.org/2021.emnlp-main.226.mp4
Code
 maoxinn/seu
Data
DBP15K