Knowledge Engineering with Big Data
IEEE Intelligent Systems2015Vol. 30(5), pp. 46–55
Citations Over TimeTop 10% of 2015 papers
Xindong Wu, Huanhuan Chen, Gongqing Wu, Jun Liu, Qinghua Zheng, Xiaofeng He, Aoying Zhou, Zhong‐Qiu Zhao, Bifan Wei, Yang Li, Qiping Zhang, Shichao Zhang
Abstract
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.
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