Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 74-85.doi: 10.11896/jsjkx.210100122

• Intelligent Computing • Previous Articles     Next Articles

Review of Reasoning on Knowledge Graph

MA Rui-xin, LI Ze-yang, CHEN Zhi-kui, ZHAO Liang   

  1. School of Software Technology,Dalian University of Technology,Dalian,Liaoning 116620,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:MA Rui-xin,born in 1975,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include big data and cloud computing.
    ZHAO Liang,born in 1988,Ph.D,associate professor.His main research inte-rests include big data and artificial intelligence.
  • Supported by:
    National Key Research and Development Program of China(2018YFC0830203),National Natural Science Foundation of China (61906030),Natural Science Foundation of Liaoning Province(2020-BS-063),Equipment Advance Research Fund(80904010301) and Fundamental Research Funds for the Central Universities(DUT20RC(4)009).

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Abstract: In recent years,the rapid development of Internet technology and reference models has led to an exponential growth in the scale of computer world data,which contains a lot of valuable information.How to select knowledge from it,and organize and express this knowledge effectively attract wide attention.Knowledge graphs are also born from this.Knowledge reasoning for knowledge graphs is one of the hotspots of knowledge graph research,and important achievements are obtained in the fields of semantic search and intelligent question answering.However,due to various defects in the sample data,such as the lack of head and tail entities in sample data,the long query path,as well as the wrong sample data.In the face of the above characteristics,the knowledge graph reasoning of zero-shot,one-shot,few-shot and multi-shot get more attention.Based on the basic concepts and basic knowledge of knowledge graph,this paper introduces the latest research progress of knowledge graph reasoning methods in recent years.Specifically,according to the size of sample data,the knowledge graph reasoning method is divided into multi-shot reasoning,few-shot reasoning,zero-shot and single-shot reasoning.Models that use more than five instances for reasoning are multi-sample reasoning,models that use two to five instances for reasoning are few-shot reasoning,and those use zero or one instance number for reasoning are zero-shpt and one-shot reasoning.The multi-shot knowledge graph reasoning is subdivided into rule-based reasoning,distributed-based reasoning,neural network-based reasoning,and other reasoning.The few-shot knowledge graph reasoning is subdivided into meta-learning-based reasoning and neighboring entity information-based reasoning.And these methods are analyzed and summarized.In addition,this paper further describes the typical application of knowledge graph reaso-ning,and discusses the existing problems,future research directions and prospects of knowledge graph reasoning.

Key words: Few-shot reasoning, Knowledge graph, Knowledge reasoning, Multi-shot reasoning, One-shot reasoning, Zero-shot reasoning

CLC Number: 

  • TP391
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