  Recent embedding-based methods have achieved great successes in exploiting
entity alignment from knowledge graph (KG) embeddings of multiple modalities.
In this paper, we study embedding-based entity alignment (EEA) from a
perspective of generative models. We show that EEA shares similarities with
typical generative models and prove the effectiveness of the recently developed
generative adversarial network (GAN)-based EEA methods theoretically. 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 (GEEA) framework with the proposed
mutual variational autoencoder (M-VAE) as the generative model. M-VAE enables
entity conversion between KGs and generation of 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.
