ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2022, Vol. 59 ›› Issue (9): 1966-1979.doi: 10.7544/issn1000-1239.20210651

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

Ning Yuanlong1, Zhou Gang1,2, Lu Jicang1, Yang Dawei1, Zhang Tian1   

  1. 1(Strategic Support Force Information Engineering University, Zhengzhou 450001);2(State Key Laboratory of Mathematical Engineering and Advanced Computing (Strategic Support Force Information Engineering University), Zhengzhou 450001)
  • Online:2022-09-01
  • Supported by: 
    This work was supported by the Science and Technology Program of Henan Province (192102210129).

Abstract: The representation learning of knowledge graph aims to map entities and relationships of knowledge graph into a continuous low-dimensional vector space through the learning method to obtain its vector representation. Most existing knowledge graph representation learning methods only consider the single-step relationship between entities from the perspective of triples, and fail to effectively use important information such as multi-step relationship paths and entity descriptions, which affects performance. In response to the above problems, we propose a knowledge graph representation learning model(PDRL) that integrates relationship paths and entity descriptions. Firstly, it is to perform a joint representation on the multi-step relationship path in the knowledge graph, and obtain the representation of the relationship path information by adding all the relationships and entities on the path; secondly, use BERT model to encode entity description information to obtain the corresponding semantic representation; finally, the fusion training is performed on the triples in the knowledge graph, the semantic representation of entity description and the representation of the relationship path to obtain the fusion vector representation. On the FB15K, WN18, FB15K-237 and WN18RR data sets, the proposed model and the benchmark model are used to perform link prediction and triple classification tasks. The experimental results show that compared with the existing benchmark models, the model in this paper has higher performance in two tasks, which proves the effectiveness and superiority of this method.

Key words: knowledge graph, representation learning, knowledge reasoning, relation path, entity description

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