THCN: A Hawkes Process Based Temporal Causal Convolutional Network for Extrapolation Reasoning in Temporal Knowledge Graphs

Temporal Knowledge Graphs (TKGs) serve as indispensable tools for dynamic facts storage and reasoning. However, predicting future facts in TKGs presents a formidable challenge due to the unknowable nature of future facts. Existing temporal reasoning models depend on fact recurrence and periodicity, leading to information degradation over prolonged temporal evolution. In particular, the occurrence of one fact may influence the likelihood of another. To this end, we propose THCN, a novel Temporal Causal Convolutional Network based on Hawkes processes, designed for temporal reasoning under the extrapolation setting. Specifically, THCN harnesses a temporal causal convolutional network with dilated factors to capture historical dependencies among facts spanning diverse time intervals. Then, we construct a conditional intensity function based on Hawkes processes for fitting the likelihood of fact occurrence. Importantly, THCN pioneers a dual-level dynamic modeling mechanism, enabling the simultaneous capture of the collective features of nodes and the individual characteristics of facts. Extensive experiments on six real-world TKG datasets demonstrate our method significantly outperforms the state-of-the-art across all four evaluation metrics, indicating that THCN is more applicable for extrapolation reasoning in TKGs.