RCTEA: Richness-guided Co-training for Temporal Entity Alignment

ACL ARR 2026 January Submission8920 Authors

Keywords: Entity Alignment, Temporal Entity Alignment, Co-training, Knowledge Representation

Abstract: 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 effect between structural and temporal features, and typically overlook the importance of information richness—a key factor for effective message passing in the neural feature encoders. To address these limitations, we propose a RCTEA framework that jointly models both structural and temporal aspects of the 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.

Paper Type: Long

Research Area: Information Extraction and Retrieval

Research Area Keywords: Information Extraction; Machine Learning for NLP; Resources and Evaluation

Contribution Types: Model analysis & interpretability, Data analysis

Languages Studied: English

Submission Number: 8920