Knowledge Engineering with Big Data

Knowledge Engineering with Big Data In the era of Big Data, knowledge engineering has to face fundamental challenges by fragmented knowledge from heterogeneous, autonomous sources with complex and evolving relationships. The knowledge representation, knowledge acquisition, and knowledge inference techniques developed in the 1970s and 1980s, driven by research and development of expert systems, need to be updated to cope with both fragmented knowledge from multiple sources in the Big Data revolution, and in-depth expertise from domain experts. This article presents BigKE, a 3-phase online learning – knowledge fusion – knowledge service” knowledge engineering framework with Big Data, with three fundamental research problems: (1) fragmented knowledge modeling and online learning from multiple information sources, (2) non-linear fusion on fragmented knowledge, and (3) automated demand-driven knowledge navigation. The knowledge graph representation is advocated in BigKE. We also compare BigKE with existing models for Big Data, such as the 4P medical model, the IBM 4V model, 5 R’s, and the HACE theorem.