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Issue No. 05 - Sept.-Oct. (2015 vol. 30)
ISSN: 1541-1672
pp: 46-55
Xindong Wu , University of Vermont, Burlington
Huanhuan Chen , University of Birmingham, Birmingham
Gongqing Wu , Hefei University of Technology, Hefei
Jun Liu , Xi'an Jiaotong University, Xi'an
Qinghua Zheng , Xi'an Jiaotong University, Xi'an
Xiaofeng He , East China Normal University, Shanghai
Aoying zhou , East China nNormal University, Shanghai
Zhong-Qiu Zhao , Hefei University of Technology, Hefei
Bifang Wei , Xi'an Jiaotong University, Xi'an
Yang Li , University of Science and Technology of China, Hefei
Qiping Zhang , Hefei University of Technology, Hefei
Shichao Zhang , Guangxi Normal University, Guilin
In the era of big data, knowledge engineering faces fundamental challenges induced by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, acquisition, and inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, must be updated to cope with both fragmented knowledge from multiple sources in the big data revolution and in-depth knowledge from domain experts. This article presents BigKE, a knowledge engineering framework that handles fragmented knowledge modeling and online learning from multiple information sources, nonlinear fusion on fragmented knowledge, and automated demand-driven knowledge navigation.
Knowledge engineering, Big data, Data models, Intelligent systems, Expert systems, Computer science, Google

X. Wu et al., "Knowledge Engineering with Big Data," in IEEE Intelligent Systems, vol. 30, no. 5, pp. 46-55, 2015.
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