Measuring Knowledge: A Quantitative Approach to Knowledge Theory
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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Author
Fred Y. Ye, School of Information Management, Nanjing University, Nanjing, China; Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing, China
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Abstract
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.
Keywords
Data, Information, Knowledge, Knowledge Metrics, Knowledge Theory
To cite this article
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. doi: 10.11648/j.ijdsa.20160202.13
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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