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- GUAN Sai-Ping
GUAN Sai-Ping
CAS Key Laboratory of Network Data Science and Technology(Institute of Computing Technology, The Chinese Academy of Sciences), Beijing 100190, China;School of Computer and Control Engineering, Unversity of Chinese Academy of Sciences, Beijing 100049, China
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Search for this author on this site - JIN Xiao-Long
JIN Xiao-Long
CAS Key Laboratory of Network Data Science and Technology(Institute of Computing Technology, The Chinese Academy of Sciences), Beijing 100190, China;School of Computer and Control Engineering, Unversity of Chinese Academy of Sciences, Beijing 100049, China
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Search for this author on this site - JIA Yan-Tao
JIA Yan-Tao
CAS Key Laboratory of Network Data Science and Technology(Institute of Computing Technology, The Chinese Academy of Sciences), Beijing 100190, China;School of Computer and Control Engineering, Unversity of Chinese Academy of Sciences, Beijing 100049, China
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Search for this author on this site - WANG Yuan-Zhuo
WANG Yuan-Zhuo
CAS Key Laboratory of Network Data Science and Technology(Institute of Computing Technology, The Chinese Academy of Sciences), Beijing 100190, China;School of Computer and Control Engineering, Unversity of Chinese Academy of Sciences, Beijing 100049, China
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Search for this author on this site - CHENG Xue-Qi
CHENG Xue-Qi
CAS Key Laboratory of Network Data Science and Technology(Institute of Computing Technology, The Chinese Academy of Sciences), Beijing 100190, China;School of Computer and Control Engineering, Unversity of Chinese Academy of Sciences, Beijing 100049, China
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National Key Research and Development Program (2016YFB1000902, 2017YFB1002302); National Natural Science Foundation of China (61772501, 61572473, 61572469, 91646120)
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In recent years, the rapid development of Internet technology and Web applications has triggered the explosion of various data on the Internet, which generates a large amount of valuable knowledge. How to organize, represent and analyze these knowledge has attracted much attention. Knowledge graph was thus developed to organize these knowledge in a semantical and visualized manner. Knowledge reasoning over knowledge graph then becomes one of the hot research topics and plays an important role in many applications such as vertical search and intelligent question-answer. The goal of knowledge reasoning over knowledge graph is to infer new facts or identify erroneous facts according to existing ones. Unlike traditional knowledge reasoning, knowledge reasoning over knowledge graph is more diversified, due to the simplicity, intuitiveness, flexibility, and richness of knowledge representation in knowledge graph. Starting with the basic concept of knowledge reasoning, this paper presents a survey on the recently developed methods for knowledge reasoning over knowledge graph. Specifically, the research progress is reviewed in detail from two aspects:One-Step reasoning and multi-step reasoning, each including rule based reasoning, distributed embedding based reasoning, neural network based reasoning and hybrid reasoning. Finally, future research directions and outlook of knowledge reasoning over knowledge graph are discussed.
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官赛萍,靳小龙,贾岩涛,王元卓,程学旗.面向知识图谱的知识推理研究进展.软件学报,2018,29(10):2966-2994
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- Received:July 20,2017
- Revised:November 08,2017
- Online: February 08,2018
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
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