Entity-Dependent Relation Learning for Knowledge Graph Completion

Abstract

Knowledge graph completion (KGC) aims to predict missing relationships between pairs of entities. Recently, Message Passing Neural Networks (MPNNs) have been employed in KGC and demonstrated superior ability in embedding learning. Current methods, however, face two major dilemmas. One focuses on learning complex entity embedding while neglecting the importance of relation embedding learning. The other is that some KGC methods conduct relation embedding learning through a neural network. However, this learning procedure limits learning independence from neighboring entities. To address this issue, we propose a Relation Entity Graph Message Passing mechanism (REGMP) to model the learning of entity-dependent relation embedding. Firstly, REGMP constructs a novel relation entity graph, which transforms relation edges into nodes with new edges and directions. The re-constructed relation entity graph builds a direct linkage between entities and relations during embedding learning. Besides, to conduct the embedding learning on the relation entity graph, we propose a Relation Entity Graph Message-passing mechanism to exploit local and fine-grained information. Moreover, to ease the inevitable over-smoothing problem, we develop a reaction-diffusion message-passing network. Experimental results on three knowledge graph completion datasets, including WN18RR, FB15k-237, and Kinship demonstrate the superiority of REGMP.