Multi-hop interpretable meta learning for few-shot temporal knowledge graph completion

Multi-hop path completion is a key part of temporal knowledge graph completion, which aims to infer complex relationships and obtain interpretable completion results. However, the traditional multi-hop path completion models mainly focus on the static knowledge graph with sufficient relationship instances, and does not consider the impact of timestamp information on the completion path, and is not suitable for few-shot relations. These limitations make the performance of these models not good when dealing with few-shot relationships in temporal knowledge graphs. In order to issue these challenges, we propose the Few-shot Temporal knowledge graph completion model based on the Multi-hop Interpretable meta-learning(FTMI). First, by aggregating the multi-hop neighbor information of the task relationship to generate a time-aware entity representation to enhance the task entity representation, the introduction of the timestamp information dimension enables the FTMI model to understand and deal with the impact of time changes on entities and relationships. In addition, time-aware entity pair representations are encoded using Transformer. At the same time, the specific representation of task relationship is generated by means of mean pooling layer aggregation. In addition, the model applies the reinforcement learning framework to the whole process of multi-hop path completion, constructs the strategy network, designs the new reward function to achieve the balance between path novelty and length, and helps Agent find the optimal path, thus realizing the completion of the temporal knowledge graph with few samples. In the training process, meta-learning is used to enable the model to quickly adapt to new tasks in the case of few samples. A huge number of experiments were carried out on two datasets to validate the model's validity.