Exploring semantic dependency for reasoning over temporal knowledge graph

Temporal knowledge graph reasoning aims to predict future facts based on given historical ones. When two entities that have had no historical interaction interact with each other, we call a new event occurs. Most existing methods suffer from limitations in predicting new events, as they only focus on direct historical interaction. To this end, we propose a temporal knowledge graph reasoning method by exploring semantic dependencies, abbreviated as SdTKG. It enriches the characterization of entities by capturing and representing potential semantic dependencies between entities with limited historical interactions. First, a potential semantic dependency graph and its encoder are constructed based on temporal path analysis to capture potential dependencies beyond direct historical interactions. Second, the entities with the similar stance to the target entity, which are mined by the potential semantic dependency graph, are used as positive samples; and then, self-supervised negative-free contrastive learning is employed on the positive samples to enhance the representation of potential semantic dependencies. Extensive experiments fully demonstrate that SdTKG is more competitive than ten state-of-the-art models. The mean reciprocal rank (MRR) values of SdTKG for relation prediction and new event prediction exceed those of the best baseline method 6. 47% and 5. 46% on average, respectively. The code is available at: https: //github. com/yuhongcqupt/SdTKG.

Authors

• Hong Yu Dalian University of Technology
• Meiqi Chen
• Ronghua Tian
• Yongfang Xie Central South University

Keywords

• Temporal knowledge graph reasoning
• Graph neural network
• Representation learning
• Potential semantic dependency