通过丰富度引导增强时序实体对齐的特征融合与鲁棒性
RCTEA: Richness-guided Co-training for Temporal Entity Alignment
- 引入丰富度感知注意力机制，动态融合结构与时序特征
- 在多个公开数据集上达到最新最佳性能，显著提升对齐准确率
- 适合处理噪声干扰下的时序知识图谱对齐任务
时序实体对齐（TEA）旨在识别跨时序知识图谱（TKG）中的等价实体，对多源知识整合至关重要。现有模型常忽略结构与时间特征的互补性，且未充分考虑信息丰富度这一影响神经编码器消息传递的关键因素。为此，我们提出RCTEA框架，联合建模TKG的结构与时间特性。设计了丰富度引导注意力机制与自适应加权策略，实现有效特征融合；引入双视角邻域共识算法，通过协同优化特征编码器，强化预测对齐的局部结构一致性，提升抗噪能力。大量实验表明，RCTEA在多个公开TEA基准上均达到领先性能。
原文摘要 · Abstract (English)
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.
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