LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation
TL
TL;DR
It is argued that existing complex EA methods inevitably inherit the inborn defects from their neural network lineage: poor interpretability and weak scalability and a neural-free EA framework is proposed — LightEA, consisting of three efficient components: Random Orthogonal Label Generation, Three-view Label Propagation, and Sparse Sinkhorn Operation.
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.
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