Unifying Invariant and Variant Knowledge With Dual‐Hypergraph Contrastive Evolution for Temporal Knowledge Graph Reasoning

Temporal knowledge graph reasoning (TKGR) excels at inferring missing event‐centric facts within a timeline, thereby mitigating the inherent incompleteness of real‐world data. Existing TKGR methods predominantly exploit intrasnapshot structural patterns and intersnapshot temporal dependencies. However, they often fail to incorporate valuable time‐invariant factual knowledge about events and struggle to jointly model entity–relation evolution dynamics. To address these challenges, we propose CKE 3 , a novel contrastive knowledge‐enhanced event evolution model that integrates time‐invariant entity background knowledge with time‐variant structural information for enhanced reasoning. Specifically, CKE 3 improves the learning of expressive entity and relation representations through an adaptive knowledge retrieval strategy, which constructs descriptive common sense knowledge associated with each entity to provide richer contextual information. Moreover, we introduce a knowledge‐aware hierarchical structural–temporal modeling module that jointly captures entity–relation structural interactions as well as sequential and periodic temporal patterns across knowledge graph snapshots. To further mitigate the impact of knowledge noise, we design a self‐supervised augmentation task that promotes the fusion of relevant information while alleviating noise. Extensive experiments on four public TKGR datasets demonstrate that CKE 3 consistently surpasses state‐of‐the‐art baselines, highlighting its effectiveness in accurate entity forecasting for the TKGR task.