Entity alignment (EA) aims to find the equivalent entity pairs between different knowledge graphs (KGs), which is crucial to promote knowledge fusion. With the wide use of temporal knowledge graphs (TKGs), time-aware EA (TEA) methods appear to enhance EA. Existing TEA models are based on Graph Neural Networks (GNN) and achieve state-of-the-art (SOTA) performance, but it is difficult to transfer them to large-scale TKGs due to the scalability issue of GNN. In this paper, we propose an effective and efficient non-neural EA framework between TKGs, namely LightTEA, which consists of four essential components: (1) Two-aspect Three-view Label Propagation, (2) Sparse Similarity with Temporal Constraints, (3) Sinkhorn Operator, and (4) Temporal Iterative Learning. All of these modules work together to improve the performance of EA while reducing the time consumption of the model. Extensive experiments on public datasets indicate that our proposed model significantly outperforms the SOTA methods for EA between TKGs, and the time consumed by LightTEA is only dozens of seconds at most, no more than 10% of the most efficient TEA method.

翻译：实体对齐旨在发现不同知识图谱间的等价实体对，对促进知识融合至关重要。随着时态知识图谱的广泛应用，时态感知实体对齐方法应运而生以增强传统实体对齐。现有基于图神经网络的时态感知实体对齐模型虽取得最先进性能，但受限于图神经网络的可扩展性问题而难以应用于大规模时态知识图谱。本文提出一种高效且有效的非神经网络实体对齐框架LightTEA，其包含四个核心组件：（1）双方面三视角标签传播，（2）含时态约束的稀疏相似度计算，（3）Sinkhorn算子，以及（4）时态迭代学习。这些模块协同作用，在降低模型时间消耗的同时提升实体对齐性能。在公开数据集上的大量实验表明，所提模型显著优于时态知识图谱间实体对齐的最先进方法，且LightTEA的最大运行时间仅需数十秒，不超过最高效时态实体对齐方法的10%。