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Cross-lingual entity alignment, which aims to precisely connect the sameentities in different monolingual knowledge bases (KBs) together, often sufferschallenges from feature inconsistency to sequence context unawareness. Thispaper presents a dual adversarial learning framework for cross-lingual entityalignment, DAEA, with two original contributions. First, in order to addressthe structural and attribute feature inconsistency between entities in twoknowledge graphs (KGs), an adversarial kernel embedding technique is proposedto extract graph-invariant information in an unsupervised manner, and projecttwo KGs into the common embedding space. Second, in order to further improvesuccessful rate of entity alignment, we propose to produce multiple randomwalks through each entity to be aligned and mask these entities in randomwalks. With the guidance of known aligned entities in the context of multiplerandom walks, an adversarial knowledge translation model is developed to filland translate masked entities in pairwise random walks from two KGs. Extensiveexperiments performed on real-world datasets show that DAEA can well solve thefeature inconsistency and sequence context unawareness issues and significantlyoutperforms thirteen state-of-the-art entity alignment methods.

Cross-lingual Entity Alignment with Adversarial Kernel Embedding and Adversarial Knowledge Translation