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    <abstract>Entity Alignment (EA) is essential for integrating Knowledge Graphs (KGs) by matching equivalent entities across diverse KGs. With the rise of multi-modal KGs, which emerged to better depict real-world KGs by integrating visual, textual, and structured data, Multi-Modal Entity Alignment (MMEA) has become crucial in enhancing EA. However, existing MMEA methods often neglect the inherent semantic category information of entities, limiting alignment precision and robustness. To address this, we propose Category-enhanced Entity Alignment (CateEA), which combines implicit entity category information into multi-modal representations. By generating pseudo-category labels from entity embeddings and integrating them into a multi-task learning framework, CateEA captures latent category semantics, enhancing entity representations. CateEA allows for adaptive adjustments of similarity measures, leading to improved alignment precision and robustness in multi-modal contexts. Experiments on benchmark datasets demonstrate that CateEA outperforms state-of-the-art methods in various settings.</abstract>
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%0 Conference Proceedings
%T CateEA: Enhancing Entity Alignment via Implicit Category Supervision
%A Feng, Guan Dong
%A Ren, Tao
%A Hu, Jun
%A Wang, Dan dan
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F feng-etal-2025-cateea
%X Entity Alignment (EA) is essential for integrating Knowledge Graphs (KGs) by matching equivalent entities across diverse KGs. With the rise of multi-modal KGs, which emerged to better depict real-world KGs by integrating visual, textual, and structured data, Multi-Modal Entity Alignment (MMEA) has become crucial in enhancing EA. However, existing MMEA methods often neglect the inherent semantic category information of entities, limiting alignment precision and robustness. To address this, we propose Category-enhanced Entity Alignment (CateEA), which combines implicit entity category information into multi-modal representations. By generating pseudo-category labels from entity embeddings and integrating them into a multi-task learning framework, CateEA captures latent category semantics, enhancing entity representations. CateEA allows for adaptive adjustments of similarity measures, leading to improved alignment precision and robustness in multi-modal contexts. Experiments on benchmark datasets demonstrate that CateEA outperforms state-of-the-art methods in various settings.
%U https://aclanthology.org/2025.coling-main.399/
%P 5975-5986

Markdown (Informal)

[CateEA: Enhancing Entity Alignment via Implicit Category Supervision](https://aclanthology.org/2025.coling-main.399/) (Feng et al., COLING 2025)

• CateEA: Enhancing Entity Alignment via Implicit Category Supervision (Feng et al., COLING 2025)

ACL

• Guan Dong Feng, Tao Ren, Jun Hu, and Dan dan Wang. 2025. CateEA: Enhancing Entity Alignment via Implicit Category Supervision. In Proceedings of the 31st International Conference on Computational Linguistics, pages 5975–5986, Abu Dhabi, UAE. Association for Computational Linguistics.