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Volume 9 Issue 8
Aug.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Contents
J. H. Lü, G. H. Wen, R. Q. Lu, Y. Wang, and S. M. Zhang, “Networked knowledge and complex networks: An engineering view,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1366–1383, Aug. 2022. doi: 10.1109/JAS.2022.105737
Citation: J. H. Lü, G. H. Wen, R. Q. Lu, Y. Wang, and S. M. Zhang, “Networked knowledge and complex networks: An engineering view,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1366–1383, Aug. 2022. doi: 10.1109/JAS.2022.105737

Networked Knowledge and Complex Networks: An Engineering View

doi: 10.1109/JAS.2022.105737
Funds:  This work was supported in part by the National Natural Science Foundation of China (61621003, 62073079, 62088101, 12025107, 11871463, 11688101)
More Information
  • 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.


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    • The state-of-the-art advances of complex networks and deep learning were briefly reviewed
    • A new framework of networked knowledge was suggested from the perspective of complex networks
    • Deep learning technologies for networked knowledge were reviewed and analyzed


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