Abstract
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.
Abstract (translated)
最近基于嵌入的方法在利用多模式知识图嵌入的实体对齐方面取得了巨大的成功。在本文中，我们从一个生成模型的角度来看研究基于嵌入的实体对齐(EEA)问题。我们表明，EEA是一个特殊的问题，其主要目标是类似于一个典型的生成模型的目标，基于这个目标，我们理论上证明了最近开发的生成对抗网络(GAN)based EEA方法的有效性。然后我们揭示了他们的不完整目标限制了实体对齐和实体生成(即生成新实体)的能力。我们通过引入一个生成EEA(暂称为GEEA)框架，以 proposed 的共变自编码器(M-VAE)作为生成模型，将从一个KG转换到另一个KG，并从随机噪声向量生成新实体。我们从理论上分析和实验上证明了GEEA的力量，同时解决了实体对齐和实体生成任务中的实体对齐和生成任务。
URL
https://arxiv.org/abs/2305.14651
