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Hybrid-patent classification based on patent-network analysis

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  • Duen-Ren Liu
  • Meng-Jung Shih

Abstract

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Suggested Citation

  • Duen-Ren Liu & Meng-Jung Shih, 2011. "Hybrid-patent classification based on patent-network analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 246-256, February.
    • Handle: RePEc:bla:jinfst:v:62:y:2011:i:2:p:246-256
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    Cited by:

    1. Ma, Tingting & Zhang, Yi & Huang, Lu & Shang, Lining & Wang, Kangrui & Yu, Huizhu & Zhu, Donghua, 2017. "Text mining to gain technical intelligence for acquired target selection: A case study for China's computer numerical control machine tools industry," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 162-180.
    2. Bekamiri, Hamid & Hain, Daniel S. & Jurowetzki, Roman, 2024. "PatentSBERTa: A deep NLP based hybrid model for patent distance and classification using augmented SBERT," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
    3. Lorenz Brachtendorf & Fabian Gaessler & Dietmar Harhoff, 2023. "Truly standard‐essential patents? A semantics‐based analysis," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 32(1), pages 132-157, January.
    4. Adam B. Jaffe & Gaétan de Rassenfosse, 2017. "Patent citation data in social science research: Overview and best practices," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(6), pages 1360-1374, June.
    5. Kamal Sanguri & Atanu Bhuyan & Sabyasachi Patra, 2020. "A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 233-269, October.
    6. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    7. Yu-Shan Chen & Chun-Yu Shih & Ching-Hsun Chang, 2014. "Explore the new relationship between patents and market value: a panel smooth transition regression (PSTR) approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1145-1159, February.

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