A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

被引:47
作者
Wu, Tianxing [1 ,2 ]
Qi, Guilin [1 ]
Li, Cheng [1 ]
Wang, Meng [1 ]
机构
[1] Southeast Univ, Sch Comp Sci & Engn, Nanjing 211189, Jiangsu, Peoples R China
[2] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
基金
中国国家自然科学基金;
关键词
knowledge graph; intelligent applications; OBOR;
D O I
10.3390/su10093245
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the "One Belt One Road (OBOR)" initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.
引用
收藏
页数:26
相关论文
共 33 条
[1]  
[Anonymous], CSWS
[2]  
[Anonymous], 2017, NATURAL LANGUAGE PRO
[3]  
[Anonymous], NATURAL LANGUAGE PRO
[4]  
[Anonymous], 2014, JOINT INT SEM TECHN
[5]  
[Anonymous], 2013, P 12 INT SEM WEB C
[6]   Linked Data - The Story So Far [J].
Bizer, Christian ;
Heath, Tom ;
Berners-Lee, Tim .
INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS, 2009, 5 (03) :1-22
[7]  
Bo Xu, 2016, Database Systems for Advanced Applications. 21st International Conference, DASFAA 2016. Proceedings: LNCS 9642, P447, DOI 10.1007/978-3-319-32025-0_28
[8]   Reducing CLASSIC to practice: Knowledge representation theory meets reality [J].
Brachman, RJ ;
McGuinness, DL ;
Patel-Schneider, PF ;
Borgida, A .
ARTIFICIAL INTELLIGENCE, 1999, 114 (1-2) :203-237
[9]   KBQA: Learning Question Answering over QA Corpora and Knowledge Bases [J].
Cui, Wanyun ;
Xiao, Yanghua ;
Wang, Haixun ;
Song, Yangqiu ;
Hwang, Seung-won ;
Wang, Wei .
PROCEEDINGS OF THE VLDB ENDOWMENT, 2017, 10 (05) :565-576
[10]  
Ding JW, 2015, AAAI CONF ARTIF INTE, P88