LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation

Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan


Abstract
Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step to bridging and integrating multi-source KGs. In this paper, we argue that existing complex EA methods inevitably inherit the inborn defects from their neural network lineage: poor interpretability and weak scalability. Inspired by recent studies, we reinvent the classical Label Propagation algorithm to effectively run on KGs and propose a neural-free EA framework — LightEA, consisting of three efficient components: (i) Random Orthogonal Label Generation, (ii) Three-view Label Propagation, and (iii) Sparse Sinkhorn Operation.According to the extensive experiments on public datasets, LightEA has impressive scalability, robustness, and interpretability. With a mere tenth of time consumption, LightEA achieves comparable results to state-of-the-art methods across all datasets and even surpasses them on many. Besides, due to the computational process of LightEA being entirely linear, we could trace the propagation process at each step and clearly explain how the entities are aligned.
Anthology ID:
2022.emnlp-main.52
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
825–838
Language:
URL:
https://aclanthology.org/2022.emnlp-main.52
DOI:
Bibkey:
Cite (ACL):
Xin Mao, Wenting Wang, Yuanbin Wu, and Man Lan. 2022. LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 825–838, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation (Mao et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.52.pdf