A Representation Learning Method of Knowledge Graph Integrating Ordered Relation Path and Entity Description Information

Abstract

Knowledge graph representation learning aims to obtain its vector representation by mapping entities and relations in knowledge graphs to a continuous low-dimensional vector space by learning methods. Most of the existing knowledge graph representation learning methods only consider the single-step relation between entities from the perspective of triples and fail to effectively utilize important information such as ordered multi-step relation paths and entity descriptions, thus affecting the ability of knowledge representation learning. We propose a knowledge graph representation learning model that integrates ordered relation paths and entity descriptions in response to the above problems. The model can integrate the triple representation in the knowledge graph, the semantic representation of entity description, and the representation of ordered relation paths for training. On the FB15K, WN18, FB15K-237, and WN18RR datasets, the proposed model and baselines are run on the link prediction task. Experimental results show that the model has higher accuracy than existing baselines, demonstrating the effectiveness and superiority of the method.