DiriE: Knowledge Graph Embedding with Dirichlet Distribution
Feiyang Wang, Zhongbao Zhang, Li Sun, Junda Ye, Yan Yang
Abstract
Knowledge graph embedding aims to learn representations of entities and relations in low-dimensional space. Recently, extensive studies combine the characteristics of knowledge graphs with different geometric spaces, including Euclidean space, complex space, hyperbolic space and others, which achieves significant progress in representation learning. However, existing methods are subject to at least one of the following limitations: 1) ignoring the uncertainty, 2) incapability of complex relation patterns. To address the above issues simultaneously, we propose a novel model named DiriE, which embeds entities as Dirichlet distributions and relations as multinomial distributions. DiriE employs Bayesian inference to measure the relations between entities and learns binary embeddings of knowledge graphs for modeling complex relation patterns. Additionally, we propose a two-step negative triple generation method that generates negative triples of both entities and relations. We conduct a solid theoretical analysis to demonstrate the effectiveness and robustness of our method, including the expressiveness of complex relation patterns and the ability to model uncertainty. Furthermore, extensive experiments show that our method outperforms state-of-the-art methods in link prediction on benchmark datasets.