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    <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.</abstract>
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%0 Conference Proceedings
%T RCTEA: Richness-guided Co-training for Temporal Entity Alignment
%A Li, Jiayun
%A Hua, Wen
%A Fan, Shiqi
%A Jin, Fengmei
%A Jiang, Haiyang
%A Li, Xue
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-rctea
%X 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.
%U https://aclanthology.org/2026.findings-acl.1958/
%P 39295-39310

Markdown (Informal)

[RCTEA: Richness-guided Co-training for Temporal Entity Alignment](https://aclanthology.org/2026.findings-acl.1958/) (Li et al., Findings 2026)

• RCTEA: Richness-guided Co-training for Temporal Entity Alignment (Li et al., Findings 2026)

ACL

• Jiayun Li, Wen Hua, Shiqi Fan, Fengmei Jin, Haiyang Jiang, and Xue Li. 2026. RCTEA: Richness-guided Co-training for Temporal Entity Alignment. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39295–39310, San Diego, California, United States. Association for Computational Linguistics.