LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation
Xin Ping Mao, Meng Wan, Yuanbin Wu, Man Lan · 2022
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 GNNbased 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.