情报研究

基于多元关系融合的专利技术演化路径识别方法研究

  • 张娴 ,
  • 曾荣强 ,
  • 李姝影 ,
  • 李嘉晖
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  • 1 中国科学院成都文献情报中心 成都 610299;
    2 中国科学院大学经济与管理学院信息资源管理系 北京 100190;
    3 成都信息工程大学计算机学院 成都 610225
张娴,研究员,博士;曾荣强,副教授,博士;李嘉晖,博士研究生。

收稿日期: 2023-05-08

  修回日期: 2023-10-04

  网络出版日期: 2024-02-23

基金资助

本文系国家社会科学基金项目“技术创新路径识别与预测的多元关系融合方法研究”(项目编号:18BTQ067)研究成果之一。

Patented Technology Evolution Path Identification Based on Multi-relation Data Fusion

  • Zhang Xian ,
  • Zeng Rongqiang ,
  • Li Shuying ,
  • Li Jiahui
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  • 1 Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610299;
    2 Department of Information Resource Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190;
    3 School of Computer Science, Chengdu University of Information Technology, Chengdu 610225

Received date: 2023-05-08

  Revised date: 2023-10-04

  Online published: 2024-02-23

Supported by

This work is supported by the National Social Science Foundation of China titled "Study on the Multi-Relation Data Fusion Methods for Identification and Prediction of Technology Innovation Paths" (Grant No. 18BTQ067).

摘要

[目的/意义] 有效融合专利引用网络中的多元关系,消除引用主观动机对技术关联真实性的影响,提高技术演化路径识别的客观性与准确性。[方法/过程] 将专利引用网络中的引用连接关系、主题关联关系、引用动机关系分别作为基础关系、增强关系和调节关系,提出一种多元关系融合的主路径识别方法,在此基础上构建基于多元关系融合的技术演化路径搜索模型,采用迭代局部搜索算法实现该模型的求解,并以石墨烯传感技术领域为例开展实证研究。[结果/结论] 对比实验证明本方法可以有效提高实证领域的技术演化路径识别效果。未来多元关系融合主路径研究深化方向是:发掘应用更多具有融合价值的主题关联类型;加强数据融合算法与模型的研究与验证。

本文引用格式

张娴 , 曾荣强 , 李姝影 , 李嘉晖 . 基于多元关系融合的专利技术演化路径识别方法研究[J]. 图书情报工作, 2024 , 68(3) : 71 -84 . DOI: 10.13266/j.issn.0252-3116.2024.03.007

Abstract

[Purpose/Significance] The effective integration of the multiple relations in the patent citation network is conducive to eliminating the influence of subjective motivation on the authenticity of technical relation and improving the objectivity and accuracy of technology evolution path identification results. [Method/Process] Taking the link connectivities, subject similarities and citing intentions in the patent citation network as the basic relations, strengthened relations and regulation relations, it proposed a main path analysis method based on multi-relation data fusion. On this basis, it constructed a technical evolution path searching model based on multi-relation data fusion, and solved the model by using an iterated local search algorithm. Then, it carried out an empirical research t in the field of graphene sensing technology as an example. [Result/Conclusion] The experimental results show that the proposed method can effectively improve the effect of technology evolution path identification in the empirical field. In the future, much work in main path of multi-relation fusion needs to be done in discovering and applying more types of theme associations with integration value, and strengthening the research and verification of data fusion algorithms and models.

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