Temporal Entity Alignment (TEA), which aims to identify equivalent entities across Temporal Knowledge Graphs (TKGs), is crucial for integrating knowledge facts from multiple sources. However, existing TEA models often fail to capture the orthogonal yet complementary effects between structural and temporal features, and typically overlook the importance of information richness, a key factor for effective message passing in neural feature encoders. To address these limitations, we propose the RCTEA framework, which jointly models both structural and temporal aspects of TKGs for entity alignment. Specifically, we design a richness-guided attention mechanism along with an adaptive weighting strategy to facilitate effective feature fusion. To ensure robust alignment despite noisy entity contexts, we introduce a dual-view neighborhood consensus algorithm that jointly refines the feature encoders to enforce local structural consistency of the predicted alignments. Extensive experiments demonstrate the superiority of RCTEA, achieving state-of-the-art performance on public TEA benchmarks.

翻译：时序实体对齐旨在跨时序知识图谱识别等价实体，对于整合多源知识事实至关重要。然而，现有时序实体对齐模型通常未能捕捉结构特征与时序特征之间正交且互补的效应，且普遍忽视信息丰富性的重要性——这一影响神经特征编码器中消息传递效能的关键因素。针对上述局限，我们提出RCTEA框架，该框架通过联合建模时序知识图谱的结构与时间维度实现实体对齐。具体而言，我们设计了一种丰富性引导的注意力机制与自适应加权策略以促进有效特征融合。为在噪声实体上下文中确保鲁棒对齐，我们引入双视角邻域共识算法，通过联合优化特征编码器来强制预测对齐的局部结构一致性。大量实验表明，RCTEA在公开时序实体对齐基准上取得最先进性能，验证了其优越性。