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Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing

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  • Taeyeoun Roh

    (Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, 26, Pil-dong 3-ga, Chung-gu, Seoul 100-715, Korea)

  • Yujin Jeong

    (Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, 26, Pil-dong 3-ga, Chung-gu, Seoul 100-715, Korea)

  • Byungun Yoon

    (Department of Industrial & Systems Engineering, School of Engineering, Dongguk University, 26, Pil-dong 3-ga, Chung-gu, Seoul 100-715, Korea)

Abstract

Since patents contain various types of objective technological information, they are used to identify the characteristics of technology fields. Text mining in patent analysis is employed in various fields such as trend analysis and technology classification, and knowledge flow among technologies. However, since keyword-based text mining has the limitation whereby, when screening useful keywords, it frequently omits meaningful keywords, analyzers therefore need to repeat the careful scrutiny of the derived keywords to clarify the meaning of keywords. In this research, we structure meaningful keyword sets related to technological information from patent documents; then we layer the keywords, depending on the level of information. This research involves two steps. First, the characteristics of technological information are analyzed by reviewing the patent law and investigating the description of patent documents. Second, the technological information is structured by considering the information types, and the keywords in each type are layered through natural language processing. Consequently, the structured and layered keyword set does not omit useful keywords and the analyzer can easily understand the meaning of each keyword.

Suggested Citation

  • Taeyeoun Roh & Yujin Jeong & Byungun Yoon, 2017. "Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:11:p:2117-:d:119377
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    References listed on IDEAS

    as
    1. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    2. Altuntas, Serkan & Dereli, Turkay & Kusiak, Andrew, 2015. "Analysis of patent documents with weighted association rules," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 249-262.
    3. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    4. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    5. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    6. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 1.
    7. Hsiu-Wen Wang & Ding-Yuan Cheng & Chi-Hua Chen & Yu-Rou Wu & Chi-Chun Lo & Hui-Fei Lin, 2015. "A Novel Real-Time Speech Summarizer System for the Learning of Sustainability," Sustainability, MDPI, vol. 7(4), pages 1-15, April.
    8. Xiaoli Guo & Huiyu Sun & Tiehua Zhou & Ling Wang & Zhaoyang Qu & Jiannan Zang, 2015. "SAW Classification Algorithm for Chinese Text Classification," Sustainability, MDPI, vol. 7(3), pages 1-15, February.
    9. Hsin-Ning Su, 2017. "Global Interdependence of Collaborative R&D-Typology and Association of International Co-Patenting," Sustainability, MDPI, vol. 9(4), pages 1-28, April.
    10. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    11. Campbell, Richard S., 1983. "Patent trends as a technological forecasting tool," World Patent Information, Elsevier, vol. 5(3), pages 137-143.
    12. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    13. Kwon, Heeyeul & Kim, Jieun & Park, Yongtae, 2017. "Applying LSA text mining technique in envisioning social impacts of emerging technologies: The case of drone technology," Technovation, Elsevier, vol. 60, pages 15-28.
    14. Lee, Woo Jin & Sohn, So Young, 2014. "Patent analysis to identify shale gas development in China and the United States," Energy Policy, Elsevier, vol. 74(C), pages 111-115.
    15. Niemann, Helen & Moehrle, Martin G. & Frischkorn, Jonas, 2017. "Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 210-220.
    16. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    17. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    18. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    19. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
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    Cited by:

    1. Roh, Taeyeoun & Yoon, Byungun, 2023. "Discovering technology and science innovation opportunity based on sentence generation algorithm," Journal of Informetrics, Elsevier, vol. 17(2).
    2. Taeyeoun Roh & Yujin Jeong & Hyejin Jang & Byungun Yoon, 2019. "Technology opportunity discovery by structuring user needs based on natural language processing and machine learning," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-27, October.

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