Temporal knowledge graphs (TKGs) effectively represent dynamic facts by incorporating a temporal dimension, yet they frequently encounter data incompleteness issues that constrain downstream applications. Concurrently, TKG prediction tasks, which enable reasoning about future events, have garnered significant attention. Existing TKG completion methods often neglect semantic information, underexploit event information from subsequent timestamps, and fail to leverage the structural symmetries inherent in temporal data. To address these limitations, this paper proposes a synergistic approach comprising two models: SiSe for completion and DL-CompGCN for prediction. SiSe integrates semantic and structural embeddings by employing entity text descriptions as semantic signals, utilizing symmetric cross-attention for bidirectional feature fusion and leveraging bidirectional gated recurrent units to capture symmetric temporal influences from both past and future events. On ICEWS14, ICEWS05-15, and GDELT completion datasets, the MRR improves by 1.2, 1.4, and 0.8 percentage points, respectively. DL-CompGCN addresses the accuracy–interpretability trade-off in prediction tasks through a time-aware graph convolutional encoder and a dual-decoder framework that combines bilinear scoring with first-order logical rules to generate interpretable paths while preserving the symmetric properties of temporal relations. It achieves state-of-the-art performance on ICEWS14, ICEWS05-15, and ICEWS18 prediction datasets. The proposed models explicitly incorporate symmetric principles in their architectural design; SiSe employs symmetric bidirectional temporal modeling, while DL-CompGCN maintains symmetry in its graph propagation and rule inference mechanisms. The experimental results demonstrate that both models significantly outperform baseline methods, offering a comprehensive solution for temporal knowledge graph reasoning that respects and exploits the symmetric structures inherent in temporal data.
Gao et al. (Wed,) studied this question.