Reliable Knowledge Graph Path Representation Learning

Knowledge graphs, which have been widely utilized in various intelligent applications, are highly incomplete. Many valid facts can be inferred from existing facts in knowledge graphs. A promising approach for this task is a knowledge graph representation learning, which aims to represent entities and relations into low-dimensional vector spaces. Most of the existing methods mainly focus on direct relationships between entities and do not reflect the semantics of multi-hop relation paths. Although a few methods have studied the problem of multi-hop path-based representation learning, they fail to distinguish reliable relation paths among a majority of meaningless relation paths. In this paper, we propose a reliable path-based knowledge graph representation learning method, called RKRL. Specifically, we combine the representations of intermediate entities and relations on relation paths to learn more meaningful knowledge representations. Also, we present a reliable knowledge graph path ranking method to avoid the unnecessary computation of unreliable paths and find semantically valid relation paths. Experimental results on benchmark datasets show that our method achieves consistent improvement on typical evaluation tasks for knowledge representations, compared with the classical and state-of-the-art representation learning baselines.