  Cross-lingual entity alignment, which aims to precisely connect the same
entities in different monolingual knowledge bases (KBs) together, often suffers
challenges from feature inconsistency to sequence context unawareness. This
paper presents a dual adversarial learning framework for cross-lingual entity
alignment, DAEA, with two original contributions. First, in order to address
the structural and attribute feature inconsistency between entities in two
knowledge graphs (KGs), an adversarial kernel embedding technique is proposed
to extract graph-invariant information in an unsupervised manner, and project
two KGs into the common embedding space. Second, in order to further improve
successful rate of entity alignment, we propose to produce multiple random
walks through each entity to be aligned and mask these entities in random
walks. With the guidance of known aligned entities in the context of multiple
random walks, an adversarial knowledge translation model is developed to fill
and translate masked entities in pairwise random walks from two KGs. Extensive
experiments performed on real-world datasets show that DAEA can well solve the
feature inconsistency and sequence context unawareness issues and significantly
outperforms thirteen state-of-the-art entity alignment methods.
