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Findings of the Association for Computational Linguistics: ACL 2022
Knowledge graph integration typically suffers from the widely existing dangling entities that cannot find alignment cross knowledge graphs (KGs). The dangling entity set is unavailable in most real-world scenarios, and manually mining the entity pairs that consist of entities with the same meaning is laborconsuming. In this paper, we propose a novel accurate Unsupervised method for joint Entity alignment (EA) and Dangling entity detection (DED), called UED. The UED mines the literal semanticdoi:10.18653/v1/2022.findings-acl.183 fatcat:of4p24ymcjcqncdshldnlmolbi