Computer Science ›› 2021, Vol. 48 ›› Issue (2): 175-189.doi: 10.11896/jsjkx.200700010

• Artificial Intelligence • Previous Articles     Next Articles

Knowledge Graph Construction Techniques:Taxonomy,Survey and Future Directions

HANG Ting-ting1,2, FENG Jun1, LU Jia-min1   

  1. 1 School of Computer and Information College,Hohai University,Nanjing 211100,China
    2 Key Laboratory of Unmanned Aerial Vehicle Development and Data Application of Anhui Higher Education Institutes,Maanshan, Anhui 243031,China
  • Received:2020-07-02 Revised:2020-10-29 Online:2021-02-15 Published:2021-02-04
  • About author:HANG Ting-ting,born in 1986,Ph.D,lecturer,is a member of China ComputerFederation.Her main research interests include domain knowledge graph construction and information extraction.
    FENG Jun,born in 1969,Ph.D,professor,Ph.D supervisor,is a professional member of China Computer Federation.Her main research interests include data management,domain knowledge discovery research,and water conservancy informatization.
  • Supported by:
    The National Key R&D Program of China(2018YFC0407901),University Natural Science Research of Anhui(KJ2019A1277) and Graduate Research Innovation Support Program of Jiangsu(2019B64214).

Abstract: With the concept of knowledge graph proposed by Google in 2012,it has gradually become a research hotspot in the field of artificial intelligence and played a role in applications such as information retrieval,question answering,and decision analysis.While the knowledge graph shows its potential in various fields,it is easy to find that there is no mature knowledge graph construction platform currently.Therefore,it is essential to research the knowledge graph construction system to meet the application needs of different industries.This paper focuses on the construction of the knowledge graph.Firstly,it introduces the current mainstream general knowledge graphs and domain knowledge graphs and describes the differences between the two in the construction process.Then,it discusses the problems and challenges in the construction of the knowledge graph according to various types.To address the above-mentioned issues and challenges,it describes the five-level solution methods and strategies of knowledge extraction,knowledge representation,knowledge fusion,knowledge reasoning,and knowledge storage in the current graph construction process.Finally,it discusses the possible directions for future research on the knowledge graph and its application.

Key words: Knowledge extraction, Knowledge fusion, Knowledge graph, Knowledge reasoning, Knowledge representation, Know-ledge storage

CLC Number: 

  • TP391.1
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