Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities. Although progresses have been achieved, existing methods are heuristically motivated and theoretical understanding of such embeddings is comparatively underdeveloped. This paper extends the random walk model (Arora et al., 2016a) of word embeddings to Knowledge Graph Embeddings (KGEs) to derive a scoring function that evaluates the strength of a relation R between two entities h (head) and t (tail). Moreover, we show that marginal loss minimisation, a popular objective used in much prior work in KGE, follows naturally from the log-likelihood ratio maximisation under the probabilities estimated from the KGEs according to our theoretical relationship. We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph. Using the derived objective, accurate KGEs are learnt from FB15K237 and WN18RR benchmark datasets, providing empirical evidence in support of the theory.

本文通过扩展随机游走模型将其用于知识图谱嵌入中，提出了一种衡量实体之间关系强度的评分函数，并给出了该方法的理论分析和学习目标。在实验中，我们使用了该方法对FB15K237和WN18RR数据集进行了准确的嵌入学习，为该理论提供了实证支持。

RelWalk：一种基于潜变量模型的知识图谱嵌入方法