An Effective and Efficient Time-aware Entity Alignment Framework via
  Two-aspect Three-view Label Propagation
Li Cai Xin Mao Youshao Xiao Changxu Wu Man Lan
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.