Advertisement

Measuring Knowledge: A Quantitative Approach to Knowledge Theory

  • Fred Y. YeEmail author
Chapter
Part of the Understanding Complex Systems book series (UCS)

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.

Notes

Acknowledgements

This chapter is a revision of the original version published at International Journal of Data Science and Analysis, 2016, 2(2): 32–35.

References

  1. Brookes, B.C.: The foundations of information science. J. Inform. Sci. 2(3/4), 125–133 (Part I) (1980); 2(5): 209–221 (Part II) (1980); 2(6): 269–275 (Part III) (1980-1981); 3(1): 3–12 (Part IV) (1981)Google Scholar
  2. Etzkowitz, H., Leydesdorff, L.: The triple helix-university-industry-government relations: a laboratory for knowledge-based economic development. EASST Rev. 14(1), 11–19 (1995)Google Scholar
  3. Frické, M.: The knowledge pyramid: a critique of the DIKW hierarchy. J. Inf. Sci. 35(2), 131–142 (2009)CrossRefGoogle Scholar
  4. Leydesdorff, L., Etzkowitz, H.: Emergence of a triple helix of university-industry-government relations. Sci. Public Policy 23(3), 279–286 (1996)Google Scholar
  5. Leydesdorff, L., Etzkowitz, H.: The triple helix as a model for innovation studies. Sci. Public Policy 25(3), 195–203 (1998)Google Scholar
  6. Mattmann, C.A.: A vision for data science. Nature 493(7433), 473–475 (2013)CrossRefGoogle Scholar
  7. Nonaka, I., von Krogh, G.: Tacit knowledge and knowledge conversion: controversy and advancement in organizational knowledge creation theory. Organ. Sci. 20(3), 635–652 (2009)CrossRefGoogle Scholar
  8. Nonaka, I., Toyama, R., Konno, N.: SECI, Ba, and leadership: a unified model of dynamic knowledge creation. Long Range Plan. 33, 5–34 (2000)CrossRefGoogle Scholar
  9. Rowley, J.: The wisdom hierarchy: representations of the DIKW hierarchy. J. Inf. Sci. 33(2), 163–180 (2007)CrossRefGoogle Scholar
  10. Shannon, C.E.: A mathematical theory of communication. Bell Sys. Techn. J. 27(3/4), 379-423, 623-656 (1948)Google Scholar
  11. Ye, F.Y.: A theoretical approach to the unification of informetric models by wave-heat equations. J. Am. Soc. Inform. Sci. Technol. 62(6), 1208–1211 (2011)CrossRefGoogle Scholar
  12. Ye, Y., Ma, F.C.: Data science: its emergence and linking with information science. J. China Soc. Sci. Tech. Inform. 34(6), 575–580 (2015)Google Scholar
  13. Ye, Y.: An analytical construction on the fundamental theory of information science and technology. J. Sci. Tech. Inform. Soci. China 18(2), 160–166 (1999)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. and Science Press 2017

Authors and Affiliations

  1. 1.Nanjing UniversityNanjingChina

Personalised recommendations