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A Method of Knowledge Extraction for Response to Rapid Technological Change with Link Mining

  • Masashi ShibataEmail author
  • Masakazu Takahashi
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 96)

Abstract

This paper proposes an efficient clustering method of technology fields for future technical trend prediction from the public information. The speed of product development is steadily improving due to the spreading of ICT as social infrastructure and the rapid progress of machine learning. However, in the process of finding a new solution to the problem, the developer’s capability is still an important. Referring to the problems and solutions to other technical fields with similar technology structure is one of the effective ways to find new solutions. However, selection of comparative fields often depends on the technical preferences and experience of developers. Thus, important signals might be overlooked. In this paper, we focus on the classification codes of patent for extracting the technology structure from the patent data. The link mining method is employed for visualizing the structure and extracting the feature. The structure is visualized as the graph of classification codes, and the feature is extracted as the features of the graph. From the result of the proposed method, we succeeded to reveal the cluster of the technology fields with similar technology structure.

Keywords

Patent Patent analyses Graph mining Clustering Technological structure analyses 

References

  1. 1.
    Rosenzweig, R.: The hazards of recombinant DNA, Stanford’s patent application natural selection effects. Trends Biochem. Sci. 2(4), 84 (1977)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Shuchman, H.L.: Engineers who patent: data from a recent survey of American bench engineers. World Patent Inf. 5(3), 174–179 (1983)CrossRefGoogle Scholar
  3. 3.
    Narin, F., Norma, E., Perry, R.: Patents as indicators of corporate technological strength. Res. Policy 16(2–4), 143–155 (1987)CrossRefGoogle Scholar
  4. 4.
    Altshuller, G.: The Innovation Algorithm: TRIZ, Systematic Innovation, and Technical Creativity. Technical Innovation Center, Worcester (1999)Google Scholar
  5. 5.
    Altshuller, G.: 40 Principles: Extended Edition. Technical Innovation Center, Worcester (2005)Google Scholar
  6. 6.
    Kawakami, H., Naito, K., Hiraoka, T., et al.: Idea generation support system for implementing benefit of inconvenience by employing the theory of inventive problem solving. Trans. Soc. Instrum. Control Eng. 49(10), 911–917 (2013)CrossRefGoogle Scholar
  7. 7.
    Kepner, C.H., Tregoe, B.B.: The Rational Manager: A Systematic Approach to Problem Solving and Decision-Making. McGraw Hill, New York (1976)Google Scholar
  8. 8.
    Kepner, C.H., Tregoe, B.B.: The New Rational Manager. Princeton Research Press, Princeton (1981)Google Scholar
  9. 9.
    Kiriyama, T.: IP information analysis (Patent information: analysis and effective utilization). J. Inf. Sci. Tech. Assoc. 60(8), 306–312 (2010)Google Scholar
  10. 10.
    Shide, K., Ando, M.: The shift of positioning of Japanese general contractors’ R&D activities. J. Archit. Plan. 76(668), 1929–1935 (2011)CrossRefGoogle Scholar
  11. 11.
    Kimura, H.: One approach of technology stocktaking and evaluation for corporate technology strategies: emphasizing future intentions and quantification through patent analysis. J. Sci. Policy Res. Manag. 26(1/2), 52–61 (2012)Google Scholar
  12. 12.
    Carpenter, M.P.: Citation rated to technologically important patents. World Patent Inf. 3(4), 160–163 (1981)CrossRefGoogle Scholar
  13. 13.
    Muguruma, M.: The usefulness of patent forward citation analysis and its practical examples. J. Inf. Sci. Tech. Assoc. 56(3), 114–119 (2006)Google Scholar
  14. 14.
    Sato, Y., Iwayama, M.: A study of patent document score using patent-specific attributes in citation analysis. J. Inf. Process. Manag. 51(5), 334–344 (2008)CrossRefGoogle Scholar
  15. 15.
    Ogawa, T., Watanabe, I.: Finding basic patents using patent citations. IPSJ SIG Notes, Inf. Process. Soc. Jpn. 35, 41–48 (2005)Google Scholar
  16. 16.
    Albert, M.B., Avery, D., Narin, F., et al.: Direct validation of citation counts as indicators of industrially important patents. Res. Policy 20(3), 251–259 (1991)CrossRefGoogle Scholar
  17. 17.
    Tanaka, K.: Multi-viewpoint clustering of patent documents. IPSJ SIG Notes 4, 9–14 (2008)Google Scholar
  18. 18.
    Yamashita, Y.: Text mining technology for patent analysis and patent search: patent search and patent analysis service patent integration. J. Inf. Process. Manag. 52(10), 581–591 (2010)CrossRefGoogle Scholar
  19. 19.
    Yamamoto, M., Maze, H., Yajima, T., et al.: A journal paper filtering using the profile revised by patent document information. IEEJ Trans. Electr. Inf. Syst. 130(2), 358–366 (2010)Google Scholar
  20. 20.
    Yamamoto, M., Kinugawa, H.: A journal paper filtering using the multiple information. IEEJ Trans. Electr. Inf. Syst. 131(6), 1250–1259 (2013)Google Scholar
  21. 21.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Eto, M.: A New Co-Citation Measure Based on Structures of Citing Papers, Database, 49(SIG 7(TOD 37)), The Information Processing Society of Japan, pp. 1–15 (2008)Google Scholar
  23. 23.
    Ueda, I.: Active mining utilizing the patent classification IPC, F1, F term on the basis of the cognitive processes of the examiner. Proc. SIG-FAI, Jpn. Soc. Artif. Intell. 46, 13–21 (2001)Google Scholar
  24. 24.
    Karamon, J., Matsuo, Y., Ishizuka, M.: Link mining from networks of academic papers. Tech. Rep. IEICE, KBSE 106(473), 73–78 (2007)Google Scholar
  25. 25.
    Kashima, H.: Mining graphs and networks. J. Inst. Electr. Inf. Commun. Eng. 93(9), 797–802 (2010)Google Scholar
  26. 26.
    Kajikawa, Y.: Utilization of citation information by link mining. J. Inf. Sci. Tech. Assoc. 60(6), 224–229 (2010)Google Scholar
  27. 27.
    Gettor, L.: Link mining: a new data mining challenge. ACM SIGKDD Explor. Newsl. 5(1), 84–89 (2003)CrossRefGoogle Scholar
  28. 28.
    Outline of FI/F-term, by The Japan Patent Office. https://www.jpo.go.jp/torikumi_e/searchportal_e/pdf/classification/fi_f-term.pdf. Accessed 23 Jan 2018
  29. 29.
    YUPASS. http://www.yupass.jp. Accessed 23 Jan 2018

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Graduate School of Sciences and Technology for InnovationYamaguchi UniversityYamaguchiJapan

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