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Empirical study of constructing a knowledge organization system of patent documents using topic modeling

Author

Listed:
  • Zhengyin Hu

    (Chengdu Document and Information Center, Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Shu Fang

    (Chengdu Document and Information Center, Chinese Academy of Sciences)

  • Tian Liang

    (Chengdu Document and Information Center, Chinese Academy of Sciences)

Abstract

A knowledge organization system (KOS) can help easily indicate the deep knowledge structure of a patent document set. Compared to classification code systems, a personalized KOS made up of topics can represent the technology information in a more agile, detailed manner. This paper presents an approach to automatically construct a KOS of patent documents based on term clumping, Latent Dirichlet Allocation (LDA) model, K-Means clustering and Principal Components Analysis (PCA). Term clumping is adopted to generate a better bag-of-words for topic modeling and LDA model is applied to generate raw topics. Then by iteratively using K-Means clustering and PCA on the document set and topics matrix, we generated new upper topics and computed the relationships between topics to construct a KOS. Finally, documents are mapped to the KOS. The nodes of the KOS are topics which are represented by terms and their weights and the leaves are patent documents. We evaluated the approach with a set of Large Aperture Optical Elements (LAOE) patent documents as an empirical study and constructed the LAOE KOS. The method used discovered the deep semantic relationships between the topics and helped better describe the technology themes of LAOE. Based on the KOS, two types of applications were implemented: the automatic classification of patents documents and the categorical refinements above search results.

Suggested Citation

  • Zhengyin Hu & Shu Fang & Tian Liang, 2014. "Empirical study of constructing a knowledge organization system of patent documents using topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 787-799, September.
    • Handle: RePEc:spr:scient:v:100:y:2014:i:3:d:10.1007_s11192-014-1328-1
    DOI: 10.1007/s11192-014-1328-1
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    Citations

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    Cited by:

    1. Suominen, Arho & Toivanen, Hannes & Seppänen, Marko, 2017. "Firms' knowledge profiles: Mapping patent data with unsupervised learning," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 131-142.
    2. Carlos Vílchez-Román & Arístides Vara-Horna, 2021. "Usage, content and citation in open access publication: any interaction effects?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9457-9476, December.
    3. Mejía, Cristian & Kajikawa, Yuya, 2019. "Technology news and their linkage to production of knowledge in robotics research," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 114-124.
    4. Qingqiang Wu & Yichen Kuang & Qingqi Hong & Yingying She, 2019. "Frontier knowledge discovery and visualization in cancer field based on KOS and LDA," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 979-1010, March.
    5. Na Kyeong Lee & Yukyeong Han & Wei Xong & Min Song, 2020. "Two layer-based trajectory analysis of the research trend in automotive fuel industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 1701-1719, September.
    6. Takano, Yasutomo & Mejia, Cristian & Kajikawa, Yuya, 2016. "Unconnected component inclusion technique for patent network analysis: Case study of Internet of Things-related technologies," Journal of Informetrics, Elsevier, vol. 10(4), pages 967-980.
    7. Righi, Riccardo & Samoili, Sofia & López Cobo, Montserrat & Vázquez-Prada Baillet, Miguel & Cardona, Melisande & De Prato, Giuditta, 2020. "The AI techno-economic complex System: Worldwide landscape, thematic subdomains and technological collaborations," Telecommunications Policy, Elsevier, vol. 44(6).

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