TECA: Temporal Evolution with Convolutional Block Attention for Knowledge Graph Reasoning

Temporal Knowledge Graph (TKG) reasoning aims to infer time-dependent facts by modeling how entities and relations evolve. Most existing methods learn entity and relation embeddings jointly from past observations and often treat relations as auxiliary signals induced by entity interactions. This coupling can over-smooth relation representations and weaken the model’s ability to capture relation-specific semantics, logical dependencies, and context shifts that are crucial for predicting future facts. To overcome this limitation, we propose TECA (Temporal Evolution with Convolutional Block Attention), a relation-centric module designed to explicitly model relation evolution in temporal graphs. TECA builds time-aware relation representations by integrating static semantics with temporal dynamics, and employs a lightweight Convolutional Block Attention Module (CBAM) that combines channel attention and spatial attention. Channel attention adaptively reweights semantic feature dimensions, while spatial attention highlights informative local interactions, enabling the model to emphasize the most discriminative relational cues under changing temporal contexts. We evaluate TECA on four widely used TKG benchmarks (ICEWS14, ICEWS18, ICEWS05–15, and GDELT) covering different temporal granularities and relational complexities. Extensive experiments and component analyses show that TECA consistently surpasses recent state-of-the-art baselines, achieving improvements of up to 6.1 MRR points and 6.3 Hits@10 points. The results indicate that explicitly enhancing relation evolution is a key factor for robust and accurate TKG reasoning.