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

2022-10-19 10:07:08
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 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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.10436

PDF

https://arxiv.org/pdf/2210.10436.pdf


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