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An improved patent similarity measurement based on entities and semantic relations

Author

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  • An, Xin
  • Li, Jinghong
  • Xu, Shuo
  • Chen, Liang
  • Sun, Wei

Abstract

Patent similarity measurement, as one of the fundamental building blocks for patent analysis, is able to derive technical intelligence efficiently, but also can detect the risk of infringement and evaluate whether the invention meets the criteria of novelty and innovation. However, traditional approaches make implicitly several assumptions, such as bag of words in each component, semantic direction irrelevance and so on. In order to relax these assumptions, this study proposes an improved methodology on the basis of entities and semantic relations (functional and non-functional relations), which takes semantic direction of each sequence structure and the word order information of each component into consideration. Meanwhile, an algorithm for calculating the global importance of each sequence structure is put forward. Finally, to verify the effectiveness and performance of the improved semantic analysis, a case study is conducted on the thin film head subfield in the field of hard disk drive. Extensive experimental results show that our approach is significantly more accurate.

Suggested Citation

  • An, Xin & Li, Jinghong & Xu, Shuo & Chen, Liang & Sun, Wei, 2021. "An improved patent similarity measurement based on entities and semantic relations," Journal of Informetrics, Elsevier, vol. 15(2).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:2:s1751157721000067
    DOI: 10.1016/j.joi.2021.101135
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    References listed on IDEAS

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    3. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Yang, Guancan & Xu, Haiyun, 2022. "A deep learning based method benefiting from characteristics of patents for semantic relation classification," Journal of Informetrics, Elsevier, vol. 16(3).
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    7. Teng, Hao & Wang, Nan & Zhao, Hongyu & Hu, Yingtong & Jin, Haitao, 2024. "Enhancing semantic text similarity with functional semantic knowledge (FOP) in patents," Journal of Informetrics, Elsevier, vol. 18(1).
    8. Shicheng Tan & Tao Zhang & Shu Zhao & Yanping Zhang, 2023. "Self-supervised scientific document recommendation based on contrastive learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5027-5049, September.
    9. Guangtong Li & L. Siddharth & Jianxi Luo, 2023. "Embedding knowledge graph of patent metadata to measure knowledge proximity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(4), pages 476-490, April.
    10. Jaewoong Choi & Jiho Lee & Janghyeok Yoon & Sion Jang & Jaeyoung Kim & Sungchul Choi, 2022. "A two-stage deep learning-based system for patent citation recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6615-6636, November.
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