RPHF-GNN: Recurrent Perception of History-Future Graph Neural Networks for Temporal Knowledge Graph Reasoning

TKG (Temporal Knowledge Graph) reasoning has become a hot research topic in recent years. its purpose is to predict the future by modeling historical information. However, existing research has primarily focused on comprehending the patterns and rules of historical facts, often overlooking the evolving trends in fact evolution driven by the emergence of new entities. This oversight poses challenges for models that learn entity and relation embeddings based on extensive historical information, ultimately resulting in a decrease in prediction accuracy. To address this challenge, we propose a graph-based neural network model, named RPHF-GNN (Recurrent Perception of History-future Graph Neural Networks). Specifically, RPHF-GNN divides the sequence into subgraph sequences of ’historical past’ and ’historical future’ at each timestep, and employs Hi-GRU (Historical-Future Information Gated Recurrent Unit) to recursively model both sequences in parallel. This allows the model to continuously perceive changes in the evolution patterns brought by unseen entities, thereby better adapting to the trends of future evolution pattern changes and enhancing the impact of Hi-GRU during the evolution process through improved Time-gate Integration Components. Additionally, in the process of constraining entity embeddings with static properties, SP-Cell (Static Perception Cell) integrates historical information from entity embeddings into the static properties to enhance the memory of the model regarding the past. It also aligns static embeddings with entity embeddings at each timestamp to optimize the static loss. We evaluate the RPHF-GNN model using six benchmark datasets, and the experimental results demonstrate significant improvement in various evaluation metrics, with the most notable enhancement reaching 1.71%.