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.
Published in | International Journal of Data Science and Analysis (Volume 2, Issue 2) |
DOI | 10.11648/j.ijdsa.20160202.13 |
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), 2016. Published by Science Publishing Group |
Data, Information, Knowledge, Knowledge Metrics, Knowledge Theory
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APA Style
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
ACS 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
@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://article.sciencepublishinggroup.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} }
TY - JOUR T1 - Measuring Knowledge: A Quantitative Approach to Knowledge Theory AU - Fred Y. Ye Y1 - 2016/12/30 PY - 2016 N1 - https://doi.org/10.11648/j.ijdsa.20160202.13 DO - 10.11648/j.ijdsa.20160202.13 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 32 EP - 36 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20160202.13 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. VL - 2 IS - 2 ER -