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

Xin Mao; Wenting Wang; Yuanbin Wu; Man Lan

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arXiv · 2210.10436

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

Xin Mao·Wenting Wang·Yuanbin Wu·Man Lan

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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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Xin Mao, Wenting Wang, Yuanbin Wu, Man Lan. 2022-10-19. LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation. https://arxiv.org/abs/2210.10436

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