IEEE/CAA Journal of Automatica Sinica
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 
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 knowledgebased 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 networkbased 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 networkbased 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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