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Measuring knowledge exploration distance at the patent level: Application of network embedding and citation analysis

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  • Choi, Jaewoong
  • Yoon, Janghyeok

Abstract

Although the incidence of knowledge exploration is observed in most patents, the concept of knowledge exploration distance has been analyzed with limited patents at the macro level of a company, organization or region. This study quantifies the knowledge exploration distance of individual patents using network embedding methods and citation analysis. First, a technology ecology network is constructed to identify technological association relationships between technical elements. Second, network embedding method is employed to represent technical elements as fixed dimensional vector, preserving the structural information. Next, the individual patents are vectorized based on the technology classification code information and pre-trained embedding values. Finally, by comparing the position between a citing patent and cited patents in the vector space, the knowledge exploration distance of the patent is obtained. This knowledge exploration distance indicates the novel degree of technological association between technical elements of a citing patent and those of cited patents. The case study covering artificial intelligence technology-related patents is conducted to illustrate the process of calculating knowledge exploration distance. Besides, this study showed that the proposed measure has significant relationships with patent-based indicators related to protection coverage, prior knowledge, and patent value.

Suggested Citation

  • Choi, Jaewoong & Yoon, Janghyeok, 2022. "Measuring knowledge exploration distance at the patent level: Application of network embedding and citation analysis," Journal of Informetrics, Elsevier, vol. 16(2).
    • Handle: RePEc:eee:infome:v:16:y:2022:i:2:s1751157722000384
    DOI: 10.1016/j.joi.2022.101286
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    2. Seol, Youngjin & Lee, Seunghyun & Kim, Cheolhan & Yoon, Janghyeok & Choi, Jaewoong, 2023. "Towards firm-specific technology opportunities: A rule-based machine learning approach to technology portfolio analysis," Journal of Informetrics, Elsevier, vol. 17(4).
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    4. Chien-chih Huang & Kuang-hua Chen, 2024. "RefCit2vec: embedding models considering references and citations for measuring document similarity," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(8), pages 4669-4693, August.

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