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Bayesian Structure Learning and Visualization for Technology Analysis

Author

Listed:
  • Sangsung Park

    (Department of Big Data and Statistics, Cheongju University, Chungbuk, Cheongju-si 28503, Korea)

  • Seongyong Choi

    (Department of Computer Engineering, Inha University, Incheon 22212, Korea)

  • Sunghae Jun

    (Department of Big Data and Statistics, Cheongju University, Chungbuk, Cheongju-si 28503, Korea)

Abstract

To perform technology analysis, we usually search patent documents related to target technology. In technology analysis using statistics and machine learning algorithms, we have to transform the patent documents into structured data that is a matrix of patents and keywords. In general, this matrix is very sparse because its most elements are zero values. The data is not satisfied with data normality assumption. However, most statistical methods require the assumption for data analysis. To overcome this problem, we propose a patent analysis method using Bayesian structure learning and visualization. In addition, we apply the proposed method to technology analysis of extended reality (XR). XR technology is integrated technology of virtual and real worlds that includes all of virtual, augmented and mixed realities. This technology is affecting most of our society such as education, healthcare, manufacture, disaster prevention, etc. Therefore, we need to have correct understanding of this technology. Lastly, we carry out XR technology analysis using Bayesian structure learning and visualization.

Suggested Citation

  • Sangsung Park & Seongyong Choi & Sunghae Jun, 2021. "Bayesian Structure Learning and Visualization for Technology Analysis," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7917-:d:594907
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    References listed on IDEAS

    as
    1. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    2. Sangsung Park & Sunghae Jun, 2020. "Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis," Sustainability, MDPI, vol. 12(2), pages 1-11, January.
    3. Sunghae Jun, 2018. "Bayesian Count Data Modeling for Finding Technological Sustainability," Sustainability, MDPI, vol. 10(9), pages 1-12, September.
    4. Davit Khachatryan & Brigitte Muehlmann, 2020. "Measuring the drafting alignment of patent documents using text mining," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-20, July.
    5. Jong-Min Kim & Bainwen Sun & Sunghae Jun, 2019. "Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    6. Sert, Onur Can & Şahin, Salih Doruk & Özyer, Tansel & Alhajj, Reda, 2020. "Analysis and prediction in sparse and high dimensional text data: The case of Dow Jones stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    Full references (including those not matched with items on IDEAS)

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