论文标题
Lightea：通过三视图标签传播的可扩展，健壮且可解释的实体对齐框架
LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation
论文作者
论文摘要
实体对齐（EA）旨在在kgs之间找到同等的实体对，这是桥接和整合多源kgs的核心步骤。在本文中，我们认为现有的基于GNN的EA方法从其神经网络谱系中继承了天生的缺陷：可扩展性较弱和可解释性差。受最近研究的启发，我们重新发明了标签繁殖算法以有效地在kgs上运行，并提出了一个非神经EA框架 - Lightea，由三个有效的组件组成：（i）随机正交标记产生，（ii）三视图标签繁殖，以及（III）Sparse Sinkhorn iTeartoration。根据公共数据集的广泛实验，Lightea具有令人印象深刻的可扩展性，鲁棒性和解释性。仅仅时间消耗的十分之一，Lightea就可以在所有数据集中取得与最先进的方法相当的结果，甚至超过许多数据集。
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.
