International Journal of Data Science and Analysis
Volume 2, Issue 2, December 2016, Pages: 32-36
Received: Oct. 6, 2016;
Accepted: Dec. 2, 2016;
Published: Dec. 30, 2016
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Fred Y. Ye, School of Information Management, Nanjing University, Nanjing, China; Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing, China
By transferring the DIKW hierarchy to the concept of chain, namely data – information – knowledge – wisdom, the knowledge measure is set up as the logarithm of information, while the information is the logarithm of data, so that knowledge metrics are naturally introduced and the mechanism of Brookes’ basic equation of information science is revealed. When knowledge is classified as explicit knowledge and tacit knowledge, qualitative SECI model is changed to quantitative triangle functions on explicit knowledge and tacit knowledge, where the former is measured by the logarithm of data and the latter is measured by the negative entropy of language. The author suggests to treat the unit of knowledge as kit, correspondingly, data as bit and information as byte.
Fred Y. Ye,
Measuring Knowledge: A Quantitative Approach to Knowledge Theory, International Journal of Data Science and Analysis.
Vol. 2, No. 2,
2016, pp. 32-36.
Copyright © 2016 Authors retain the copyright of this article.
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