TARGAT: A Time-Aware Relational Graph Attention Model for Temporal Knowledge Graph Embedding

作者：Xie, Zhiwen; Zhu, Runjie; Liu, Jin^*; Zhou, Guangyou^*; Huang, Jimmy Xiangji

来源：IEEE-ACM Transactions on Audio Speech and Language Processing, 2023, 31: 2246-2258.

DOI：10.1109/TASLP.2023.3282101

• Graph embedding
• link prediction
• knowledge graph
• representation learning

Temporal knowledge graph embedding (TKGE) aims to learn the embedding of entities and relations in a temporal knowledge graph (TKG). Although the previous graph neural networks (GNN) based models have achieved promising results, they cannot directly capture the interactions of multi-facts at different timestamps. To address the above limitation, we propose a time-aware relational graph attention model (TARGAT), which takes the multi-facts at different timestamps as a unified graph. First, we develop a relational generator to dynamically generate a series of time-aware relational message transformation matrices, which jointly models the relations and the timestamp information into a unified way. Then, we apply the generated message transformation matrices to project the neighborhood features into different time-aware spaces and aggregate these neighborhood features to explicitly capture the interactions of multi-facts. Finally, a temporal transformer classifier is applied to learn the representation of the query quadruples and predict the missing entities. The experimental results show that our TARGAT model beats the GNN-based models by a large margin and achieves new state-of-the-art results on four popular benchmark datasets.

• 单位
  Wuhan University; 1