MPNet: temporal knowledge graph completion based on a multi-policy network

Temporal knowledge graphs completion (TKGC) is a critical task that aims to forecast facts that will occur in future timestamps. It has attracted increasing research interest in recent years. Among the many approaches, reinforcement learning-based methods have gained attention due to their efficient performance and interpretability. However, these methods still face two challenges in the prediction task. First, a single policy network lacks the capability to capture the dynamic and static features of entities and relationships separately. Consequently, it fails to evaluate candidate actions comprehensively from multiple perspectives. Secondly, the composition of the action space is incomplete, often guiding the agent towards distant historical events and missing the answers in recent history. To address these challenges, this paper proposes a Temporal Knowledge Graph Completion Based on a Multi-Policy Network(MPNet). It constructs three policies from the aspects of static entity-relation, dynamic relationships, and dynamic entities, respectively, to evaluate candidate actions comprehensively. In addition, this paper creates a more diverse action space that guides the agent in investigating answers within historical subgraphs more effectively. The effectiveness of MPNet is validated through an extrapolation setting, and extensive experiments conducted on three benchmark datasets demonstrate the superior performance of MPNet compared to existing state-of-the-art methods.