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

• URL: http://arxiv.org/abs/2210.10436v2
• Date: Thu, 20 Oct 2022 05:14:04 GMT
• Title: LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation
• Authors: Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan
• Abstract summary: We argue that existing GNN-based EA methods inherit the inborn defects from their neural network lineage: weak scalability and poor interpretability. We propose a non-neural EA framework -- LightEA, consisting of three efficient components: (i) Random Orthogonal Label Generation, (ii) Three-view Label propagation, and (iii) Sparse Sinkhorn Iteration. According to the extensive experiments on public datasets, LightEA has impressive scalability, robustness, and interpretability.
• Score: 27.483109233276632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step of bridging and integrating multi-source KGs. In this paper, we argue that existing GNN-based EA methods inherit the inborn defects from their neural network lineage: weak scalability and poor interpretability. Inspired by recent studies, we reinvent the Label Propagation algorithm to effectively run on KGs and propose a non-neural EA framework -- LightEA, consisting of three efficient components: (i) Random Orthogonal Label Generation, (ii) Three-view Label Propagation, and (iii) Sparse Sinkhorn Iteration. 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.

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