{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:46:19Z","timestamp":1781196379530,"version":"3.54.1"},"reference-count":50,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306079"],"award-info":[{"award-number":["62306079"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023GXJK422"],"award-info":[{"award-number":["2023GXJK422"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2024KYCXTD015"],"award-info":[{"award-number":["2024KYCXTD015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Education Science Planning Project of Guangdong Province (CN)","award":["62306079"],"award-info":[{"award-number":["62306079"]}]},{"name":"Education Science Planning Project of Guangdong Province (CN)","award":["2023GXJK422"],"award-info":[{"award-number":["2023GXJK422"]}]},{"name":"Education Science Planning Project of Guangdong Province (CN)","award":["2024KYCXTD015"],"award-info":[{"award-number":["2024KYCXTD015"]}]},{"name":"Scientific Research Innovation Team Project of Guangdong University of Education","award":["62306079"],"award-info":[{"award-number":["62306079"]}]},{"name":"Scientific Research Innovation Team Project of Guangdong University of Education","award":["2023GXJK422"],"award-info":[{"award-number":["2023GXJK422"]}]},{"name":"Scientific Research Innovation Team Project of Guangdong University of Education","award":["2024KYCXTD015"],"award-info":[{"award-number":["2024KYCXTD015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The success of current entity alignment (EA) tasks largely depends on the supervision information provided by labeled data. Considering the cost of labeled data, most supervised methods are challenging to apply in practical scenarios. Therefore, an increasing number of works based on contrastive learning, active learning, or other deep learning techniques have been developed, to solve the performance bottleneck caused by the lack of labeled data. However, existing unsupervised EA methods still face certain limitations; either their modeling complexity is high or they fail to balance the effectiveness and practicality of alignment. To overcome these issues, we propose a learnable convolutional attention network for unsupervised entity alignment, named LCA-UEA. Specifically, LCA-UEA performs convolution operations before the attention mechanism, ensuring the acquisition of structural information and avoiding the superposition of redundant information. Then, to efficiently filter out invalid neighborhood information of aligned entities, LCA-UEA designs a relation structure reconstruction method based on potential matching relations, thereby enhancing the usability and scalability of the EA method. Notably, a similarity function based on consistency is proposed to better measure the similarity of candidate entity pairs. Finally, we conducted extensive experiments on three datasets of different sizes and types (cross-lingual and monolingual) to verify the superiority of LCA-UEA. Experimental results demonstrate that LCA-UEA significantly improved alignment accuracy, outperforming 25 supervised or unsupervised methods, and improving by 6.4% in Hits@1 over the best baseline in the best case.<\/jats:p>","DOI":"10.3390\/e27090924","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T08:04:15Z","timestamp":1756886655000},"page":"924","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learnable Convolutional Attention Network for Unsupervised Knowledge Graph Entity Alignment"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2416-6841","authenticated-orcid":false,"given":"Weishan","family":"Cai","sequence":"first","affiliation":[{"name":"School of Computer Science, Guangdong University of Education, Guangzhou 510303, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjun","family":"Ma","sequence":"additional","affiliation":[{"name":"Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Foshan 528225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhao, J., Zhang, P., Wang, Y., Xin, R., Lu, X., Li, R., Lyu, S., Ou, Z., and Song, M. 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