Recent embedding-based methods have achieved great successes on exploiting entity alignment from knowledge graph (KG) embeddings of multiple modals. In this paper, we study embedding-based entity alignment (EEA) from a perspective of generative models. We show that EEA is a special problem where the main objective is analogous to that in a typical generative model, based on which we theoretically prove the effectiveness of the recently developed generative adversarial network (GAN)-based EEA methods. We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i.e., generating new entities). We mitigate this problem by introducing a generative EEA (abbr., GEEA) framework with the proposed mutual variational autoencoder (M-VAE) as the generative model. M-VAE can convert an entity from one KG to another and generate new entities from random noise vectors. We demonstrate the power of GEEA with theoretical analysis and empirical experiments on both entity alignment and entity synthesis tasks.

本文从生成模型的角度研究了基于嵌入的实体对齐（EEA），理论上证明了生成对抗网络（GAN）方法的有效性，并提出了基于互相变分自编码器（M-VAE）的生成EEA（GEEA）框架，可以在实体对齐和实体合成任务中产生新实体。

重访和超越实体对齐: 生成模型的视角