International Journal of Data Science and Analysis

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Measuring Knowledge: A Quantitative Approach to Knowledge Theory

Received: 06 October 2016    Accepted: 02 December 2016    Published: 30 December 2016
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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.

DOI 10.11648/j.ijdsa.20160202.13
Published in International Journal of Data Science and Analysis (Volume 2, Issue 2, December 2016)
Page(s) 32-36
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Data, Information, Knowledge, Knowledge Metrics, Knowledge Theory

References
[1] Brookes, B. C. (1980-1981). The Foundations of Information Science. Journal of Information Science, 1980, 2 (3-4), 125-133 (Part I); 1980, 2 (5), 209-221 (Part II); 1980, 2 (6), 269-275 (Part III); 1981, 3 (1), 3-12 (Part IV).
[2] Etzkowitz H, Leydesdorff L (1995). The Triple Helix--University-Industry-Government Relations: A Laboratory for Knowledge-Based Economic Development. EASST Review, 14 (1), 11-19.
[3] Frické, M. (2009). The knowledge pyramid: a critique of the DIKW hierarchy. Journal of Information Science, 35 (2), 131-142.
[4] Leydesdorff L, Etzkowitz H. (1996). Emergence of a Triple Helix of University-Industry-Government Relations. Science and Public Policy, 23 (3), 279-286.
[5] Leydesdorff L, Etzkowitz H. (1998). The Triple Helix as a model for innovation studies. Science and Public Policy, 25 (3), 195-203.
[6] Mattmann, C. A. (2013). A vision for data science. Nature, 493 (7433), 473-475.
[7] Nonaka, I., Toyama, R. and Konno, N. (2000). SECI, Ba, and leadership: a unified model of dynamic knowledge creation. Long Range Planning, 33, 5-34.
[8] Nonaka, I. and von Krogh, G. (2009). Tacit Knowledge and Knowledge Conversion: Controversy and Advancement in Organizational Knowledge Creation Theory. Organization Science. 20 (3), 635–652.
[9] Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy [J]. Journal of Information Science, 33 (2), 163-180.
[10] Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27 (3-4), 379-423, 623-656.
[11] Ye, Y. (1999). An analytical construction on the fundamental theory of information science and technology. Journal of Scientific and Technical Information Society of China, 18 (2), 160-166.
[12] Ye, F. Y. (2011). A Theoretical Approach to the Unification of Informetric Models by Wave-heat Equations. Journal of the American Society for Information Science and technology, 62 (6), 1208-1211.
[13] Ye, Y and Ma, F.-C. (2015). Data science: its emergence and linking with information science. Journal of the China Society for Scientific and Technical Information, 34 (6): 575–580.
Author Information
  • School of Information Management, Nanjing University, Nanjing, China; Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing, China

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    Fred Y. Ye. (2016). Measuring Knowledge: A Quantitative Approach to Knowledge Theory. International Journal of Data Science and Analysis, 2(2), 32-36. https://doi.org/10.11648/j.ijdsa.20160202.13

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    Fred Y. Ye. Measuring Knowledge: A Quantitative Approach to Knowledge Theory. Int. J. Data Sci. Anal. 2016, 2(2), 32-36. doi: 10.11648/j.ijdsa.20160202.13

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    AMA Style

    Fred Y. Ye. Measuring Knowledge: A Quantitative Approach to Knowledge Theory. Int J Data Sci Anal. 2016;2(2):32-36. doi: 10.11648/j.ijdsa.20160202.13

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  • @article{10.11648/j.ijdsa.20160202.13,
      author = {Fred Y. Ye},
      title = {Measuring Knowledge: A Quantitative Approach to Knowledge Theory},
      journal = {International Journal of Data Science and Analysis},
      volume = {2},
      number = {2},
      pages = {32-36},
      doi = {10.11648/j.ijdsa.20160202.13},
      url = {https://doi.org/10.11648/j.ijdsa.20160202.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijdsa.20160202.13},
      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.},
     year = {2016}
    }
    

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    AB  - 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.
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