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Patent representation learning with a novel design of patent ontology: Case study on PEM patents

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  • Zhai, Dongsheng
  • Zhai, Liang
  • Li, Mengyang
  • He, Xijun
  • Xu, Shuo
  • Wang, Feifei

Abstract

Under the background of innovation-driven knowledge economy globalization, comprehensive and insightful patent technology information mining can help enterprises win the first-mover advantage in the increasingly fierce technology competition. However, existing machine learning-based methods do not entirely incorporate the characteristics of patent technology of technology composition and technology association at the micro-level and macro-level, making it difficult to mine detailed and comprehensive patent information. To fill this research gap, firstly, we conduct a comprehensive analysis from the micro-level technology composition perspective of patent documents and the macro-level technology association perspective of patent data involved in the technology field, and then we design a novel patent ontology that includes the entity of patent, function, solution and application field. Secondly, we create a patent heterogeneous network with the help of the proposed patent ontology and the technology association. Finally, to fully use the patent technology characteristics, we develop a heterogeneous graph embedding algorithm to embed this information into the patent representation, and the experiments done on non-perfluorinated proton exchange membrane patent data show that our method produces better patent representation than the comparable models. Furthermore, we utilize the patent representation to perform case study to confirm the method’s reliability and practicability.

Suggested Citation

  • Zhai, Dongsheng & Zhai, Liang & Li, Mengyang & He, Xijun & Xu, Shuo & Wang, Feifei, 2022. "Patent representation learning with a novel design of patent ontology: Case study on PEM patents," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:tefoso:v:183:y:2022:i:c:s0040162522004346
    DOI: 10.1016/j.techfore.2022.121912
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    References listed on IDEAS

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