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.

翻译：近年来，基于嵌入的方法在利用多模态知识图谱嵌入进行实体对齐方面取得了显著成功。本文从生成模型的视角研究基于嵌入的实体对齐（EEA）。我们证明EEA与典型生成模型具有相似性，并从理论上验证了近期发展的生成对抗网络（GAN）方法在EEA中的有效性。进而揭示其不完整的目标函数限制了实体对齐和实体合成（即生成新实体）的能力。通过引入生成式EEA（GEEA）框架，并以所提出的互变分自编码器（M-VAE）作为生成模型来缓解该问题。M-VAE支持知识图谱间的实体转换以及从随机噪声向量生成新实体。我们通过理论分析与实体对齐及实体合成任务的实证实验，展示了GEEA的强大性能。