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Recently, embedding-based entity pair similarity evaluation has become mainstream in entity alignment research. However, these methods heavily rely on labelled entity pairs, which are often unavailable. Some self-supervised methods exploit features of KGs regardless of noise when generating aligned entity pairs. To resolve this issue, we propose a generative adversarial entity alignment method, which is more robust to noise data. The proposed method then exploits both attribute and structure information in the KGs and applies a BERT-based contrastive loss function to embed entities in KGs. Experimental results on several benchmark datasets demonstrate the superiority of our framework compared with most existing state-of-the-art entity alignment methods.<\/jats:p>","DOI":"10.1007\/s11280-023-01140-8","type":"journal-article","created":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T09:50:37Z","timestamp":1676109037000},"page":"2265-2290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Generative adversarial network for unsupervised multi-lingual knowledge graph entity alignment"],"prefix":"10.1007","volume":"26","author":[{"given":"Yunfei","family":"Li","sequence":"first","affiliation":[]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chengfei","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,11]]},"reference":[{"key":"1140_CR1","doi-asserted-by":"publisher","unstructured":"Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.G.: Dbpedia: a nucleus for a Web of open data. 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