Computer Science ›› 2023, Vol. 50 ›› Issue (3): 94-113.doi: 10.11896/jsjkx.220900136

• Special Issue of Knowledge Engineering Enabled By Knowledge Graph: Theory, Technology and System • Previous Articles     Next Articles

Survey of Knowledge Graph Reasoning Based on Representation Learning

LI Zhifei, ZHAO Yue, ZHANG Yan   

  1. School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China
  • Received:2022-09-15 Revised:2022-12-01 Online:2023-03-15 Published:2023-03-15
  • About author:LI Zhifei,born in 1993,Ph.D,lecture,is a member of China Computer Federation.His main research interests include knowledge graphs and graph neural networks.
  • Supported by:
    National Natural Science Foundation of China(62207011,61977021,62101179),Key Project of Technology Innovation in Hubei Province(2019ACA144) and School Project of Hubei University(202111903000001,202011903000002).

Abstract: Knowledge graphs describe objective knowledge in the real world in a structured form,and are confronted with issues of completeness and newly-added knowledge.As an important means of complementing and updating knowledge graphs,know-ledge graph reasoning aims to infer new knowledge based on existing knowledge.In recent years,the research on knowledge graph reasoning based on repre-sentation learning has received extensive attention.The main idea is to convert the traditional reasoning process into semantic vector calculation based on the distributed representation of entities and relations.It has the advantages of fast calculation efficiency and high reasoning performance.In this paper,we review the knowledge graph reasoning based on repre sentation learning.Firstly,this paper summarizes the symbolic representation,data set,evaluation metric,training method,and evaluation task of knowledge graph reasoning.Secondly,it introduces the typical methods of knowledge graph reasoning,including translational distance and semantic matching methods.Thirdly,multi-source information fusion-based knowledge graph reasoning methods are classified.Then,neural network-based reasoning methods are introduced including convolutional neural network,graph neural network,recurrent neural network,and capsule network.Finally,this paper summarizes and forecasts the future research direction of knowledge graph reasoning.

Key words: Representation learning, Knowledge graph reasoning, Translational distance, Semantic matching, Multi-source information, Neural network

CLC Number: 

  • TP391
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