TY - JOUR
T1 - Networked Knowledge and Complex Networks
T2 - An Engineering View
AU - Lu, Jinhu
AU - Wen, Guanghui
AU - Lu, Ruqian
AU - Wang, Yong
AU - Zhang, Songmao
N1 - Publisher Copyright:
© 2014 Chinese Association of Automation.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - Along with the development of information technologies such as mobile Internet, information acquisition technology, cloud computing and big data technology, the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role. Within this context, it is required to develop new methodologies as well as technical tools for network-based knowledge representation, knowledge services and knowledge engineering. Obviously, the term 'network' has different meanings in different scenarios. Meanwhile, some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation, knowledge services and knowledge engineering. This paper first reviews some recent advances on complex networks, and then, in conjunction with knowledge graph, proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks. For the unique advantages of deep learning in acquiring and processing knowledge, this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering. Finally, some challenges and further trends are discussed.
AB - Along with the development of information technologies such as mobile Internet, information acquisition technology, cloud computing and big data technology, the traditional knowledge engineering and knowledge-based software engineering have undergone fundamental changes where the network plays an increasingly important role. Within this context, it is required to develop new methodologies as well as technical tools for network-based knowledge representation, knowledge services and knowledge engineering. Obviously, the term 'network' has different meanings in different scenarios. Meanwhile, some breakthroughs in several bottleneck problems of complex networks promote the developments of the new methodologies and technical tools for network-based knowledge representation, knowledge services and knowledge engineering. This paper first reviews some recent advances on complex networks, and then, in conjunction with knowledge graph, proposes a framework of networked knowledge which models knowledge and its relationships with the perspective of complex networks. For the unique advantages of deep learning in acquiring and processing knowledge, this paper reviews its development and emphasizes the role that it played in the development of knowledge engineering. Finally, some challenges and further trends are discussed.
KW - Complex network
KW - knowledge graph
KW - networked knowledge
KW - neural network
UR - https://www.scopus.com/pages/publications/85135754280
U2 - 10.1109/JAS.2022.105737
DO - 10.1109/JAS.2022.105737
M3 - 文章
AN - SCOPUS:85135754280
SN - 2329-9266
VL - 9
SP - 1366
EP - 1383
JO - IEEE/CAA Journal of Automatica Sinica
JF - IEEE/CAA Journal of Automatica Sinica
IS - 8
ER -