Guiding Crosslingual Entity Alignment via Adversarial Knowledge Embedding Crosslingual Entity Alignment (CEA) aims at identifying entities with their counterparts in different language knowledge graphs. Knowledge embedding alignment plays an important role in CEA due to its advantages of easy implementation and runtime robustness. However, existing embedding alignment methods havenxe2x80x99t considered the problem of embedding distribution alignment which refers to the alignment of spatial shapes of embedding spaces. To this end, we present a new Adversarial Knowledge Embedding framework (AKE for short) that jointly learns the representation, mapping and adversarial modules in an endtoend manner. By reducing the discrepancy of embedding distributions, AKE can approximately preserve an isomorphism between source and target embeddings. In addition, we introduce two new orthogonality constraints into mapping to obtain the selfconsistency and numerical stability of transformation. Experiments on realworld datasets demonstrate that our method significantly outperforms stateoftheart baselines.

Dual Adversarial Learning Based Network Alignment Network alignment, which aims to learn a matching between the same entities across multiple information networks, often suffers challenges from feature inconsistency, highdimensional features, to unstable alignment results. This paper presents a novel network alignment framework, RANA, that combines dual generative adversarial network (GAN) techniques to match the distributions of two networks based on two dimensions of distance and shape. First, we propose an adversarial network distribution matching model to perform the bidirectional crossnetwork alignment translations between two networks, such that the crossnetwork transformed distributions of two networks move closer to each other and finally meet with each other halfway. In addition, a homophily consistency loss is introduced to maintain the vertex homophily consistency between pairwise vertices on two networks in both the embedding space. Second, in order to address the feature inconsistency issue, we integrate a dual adversarial autoencoder module with an adversarial twoclass classification model together to twist the crossnetwork transformed distributions of two networks, such that two distributions could have the same shape. This facilitates the translations of the distributions of two networks in the adversarial network distribution matching model. Moreover, a semantic preservation loss is introduced to preserve the original embedding semantics of one network when this network is translated to another network and returned to itself. Third but last, the competition game by integrating the above two adversarial models together can help project two copies of the same vertices with highdimensional inconsistent features into the same lowdimensional embedding space, and thus guarantee the distribution consistency between two networks in terms of both distance and shape.