An effective Time-Aware Encoder for Temporal Knowledge Graph Reasoning
Hao Duan, Haoyu Jin, Kang Chen, Shaochong Du, Tao Fang, Hong Huo · 2022
There are many studies on static knowledge graph (KG) reasoning that predicts missing facts for its completeness. As the facts in real world usually are time-dependent, temporal knowledge graph (TKG) has received great attention lately. Entities and their relations in TKGs may change over time, and how to predict future facts from past facts has become a fundamental subject of TKGs. In this paper, a novel effective time-aware encoder (TAE) is proposed for TKG reasoning. It encodes the influence of time on entities and relations into accurate time-specific embedding representations. Then the embedding representations of entities and relations under different timestamps are employed for the prediction of future facts. The evaluations on four public TKG datasets have demonstrated that TAE outperforms all baseline models on TKG reasoning tasks.