Knowledge Engineering with Big Data

Abstract

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 3phase "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.

A Novel Variable Precision Reduction Approach to Comprehensive Knowledge Systems

“…To illustrate the effectiveness and performance of the proposed CVPR algorithm, we evaluate its performance on 30 well-known benchmark datasets from the UC Irvine machine learning repository 1 . Some of these datasets (e.g.…”

An Entropy Approach to China Big Data Ecology

“…In this paper, the business decision process of Big Data in the current stage is summarized as follows: Target requirements, Algorithm design, Data storage, Data preprocessing, Data interpretation, and Quantitative decision making. Based on relative theories such as 4Vs (Volume, Variety , Velocity, Value (Cukier & Schönberger, 2013)), 5Vs (Volu me, Velocity, Variety, Value, Veracity (Bernard, 2014)), 5Rs (Relevant, Real-time, Realistic, Return on investment (Stidston, 2014)), HACE (Heterogeneity, Autonomy, Complexit y , Evolution (Wu, Zhu, Wu, & Ding, 2014)), and BigKE (Big Data Knowledge Engineering (Wu et al, 2015)), this paper defines the nature and scope of Big Data as follows: Big Data, whose nature is data and information, is a series of procedure for processing, computing and storing an endless stream of data through a medium of transmission by means of a predetermined set of information gathering methodology.…”

Theoretical aspects of the information and knowledge engineering

“…[25] states that "This fragmented knowledge is part of the migration puzzle-each piece provides some limited information, but not the whole picture. Traditional knowledge engineering can't obtain and process such fragmented knowledge because it's usually acquired from different sources.…”

“…8. Big KE - [25] The figure 8 [25] exemplifies the evolution of the KE as a demand from society needs of information and knowledge usage that is evolving with the Technologies and the digital era.…”