MuHi-Net: Multi-History Perspective Network for Temporal Knowledge Reasoning

Reasoning on Temporal knowledge graphs (TKGs) aims to predict future events based on historical cues of entities and their relations. However, most existing reasoning methods usually represent the knowledge timeline as discrete points and focus on reasoning from the single-history perspective, such as repetitive events or query-related first-order historical information, which greatly limit the improvement of reasoning accuracy, especially for fine-grained TKGs with dense timestamps. To address these issues, Multi-History Perspective Network (MuHiNet) is proposed for temporal knowledge reasoning on TKGs, and it takes three historical perspectives into reasoning, namely neighbor history, repetitive history and sequential history, which directly obtains entity representations at any temporal granularity through dynamic entity embeddings for the prediction of future events. It represents entity embeddings as continuous functions of time. Furthermore, it considers the influence of time duration to derive the final entity representations. MuHi-Net contains four modules, the base intensity module, the neighbor historical intensity module, the repetitive historical intensity module and sequential historical intensity module. The base score of event occurrence is firstly calculated by the base module, then the impact of historical facts on the current event are evaluated by the other three modules. The final inference result is obtained based on integrating scores from these four modules. The experiments on three datasets have highlighted the superiority of MuHi-Net. Specifically, on self-built SMOE dataset, MuHi-Net has achieved significant improvement on predictive accuracy in scenarios with dense timestamps by effectively utilizing historical information from multiple perspectives.