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数据分析与知识发现  2023, Vol. 7 Issue (2): 119-128     https://doi.org/10.11925/infotech.2096-3467.2022.0330
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
创新二重性视角下明星发明人类型的早期识别*
刘向(),刘香,余博文
华中师范大学信息管理学院 武汉 430079
Early Identification of Star Inventor Types in the Perspective of Innovation Duality
Liu Xiang(),Liu Xiang,Yu Bowen
School of Information Management, Central China Normal University, Wuhan 430079, China
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摘要 

目的】 通过专利数量和专利引用识别明星发明人类型的方法存在明显时滞效应,本文结合专利文本和发明者合作关系构建了图卷积神经网络,该模型可以用于明星发明人的早期识别。【方法】 从“延续性创新”、“突破性创新”两个维度将明星发明人的创新类型分为“复合型”、“巩固型”、“突破型”和“发展型”4类,结合专利标题信息和明星发明人的合作关系,构建基于图卷积神经网络的明星发明人类型的早期识别模型。【结果】 以分子生物学与微生物学领域内专利数据进行了验证,实验表明本模型识别明星发明人创新类型的整体准确率为79.4%,相较于只使用词向量的方法准确率提高了约15个百分点。【局限】 本文模型对于“突破型明星发明人”早期识别效果不理想,还需进一步寻找突破型发明人的特征,以提高模型的有效性。【结论】 本文模型可以克服基于专利数量和引证的识别方法的时滞效应,能尽早地识别明星发明人的创新类型。

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刘向
刘香
余博文
关键词 明星发明人创新二重性早期识别突破性创新延续性创新合作关系    
Abstract

[Objective] Identifying the star inventors by the number of patents and patent citations has obvious time lag effects. Therefore, this paper constructs a graph convolutional neural network to find the emerging star inventors effectively. [Methods] This paper defines four types of star inventors: “composite”, “consolidation”, “breakthrough” and “development” which can also be grouped as “continuity innovation” and “breakthrough innovation”. Then, we constructed a model based on graph convolutional neural network combining patent titles and the cooperation relationship to find star inventors. [Results] We examined our model with patent data in the field of molecular biology and microbiology. The overall accuracy of this model in identifying the innovation types of star inventors reached 79.4%, which was about 15% higher than the method using word vectors. [Limitations] The proposed model could not identify “breakthrough star inventors” effectively. [Conclusions] Our new model could reduce the time-lag effect of the existing methods and identify the innovation type of star inventors earlier.

Key wordsStar Inventors    Duality of Innovation    Early Recognition    Breakthrough Innovation    Continuous Innovation    Relations of Cooperation
收稿日期: 2022-04-11      出版日期: 2023-03-28
ZTFLH:  G305 TP183  
基金资助:*国家自然科学基金项目的研究成果之一(71673106)
通讯作者: 刘向,ORCID:0000-0003-4315-2699,E-mail: xiangliu@mail.ccnu.edu.cn。   
引用本文:   
刘向, 刘香, 余博文. 创新二重性视角下明星发明人类型的早期识别*[J]. 数据分析与知识发现, 2023, 7(2): 119-128.
Liu Xiang, Liu Xiang, Yu Bowen. Early Identification of Star Inventor Types in the Perspective of Innovation Duality. Data Analysis and Knowledge Discovery, 2023, 7(2): 119-128.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2022.0330      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2023/V7/I2/119
Fig.1  明星发明人的4种创新类型
Fig.2  明星发明人创新类型早期识别模型
序号 名词 动词 其他
主题词 频次 主题词 频次 主题词 频次
1 method 25 436 produce 5 066 with 3 331
2 cell 8 135 encode 2 749 or 2 580
3 acid 7 081 prepare 2 696 recombinant 1 708
4 protein 5 770 detect 2 456 specific 1 646
5 process 5 327 identify 2 072 basic 1 280
6 nucleic 4 453 determine 2 000 associated 904
7 gene 4 113 contain 1 549 related 839
8 composition 3 877 bind 1 465 non 781
9 production 3 672 modify 1 150 isolated 705
10 detection 3 629 enhance 645 which 572
Table 1  明星发明人的特征词示例
Fig.3  明星发明人延续性创新值和突破性创新值分布
序号 发明人 method methods acid process nucleic ……
1 Kenneth W. Kinzler 1 1 1 1 1 ……
2 Bert Vogelstein 1 1 1 0 1 ……
3 Xu Hong 1 1 1 0 1 ……
4 Li Xiaodong 1 1 1 0 1 ……
5 Robert Dicosimo 0 0 1 1 0 ……
Table 2  明星发明人的特征矩阵构建示例
序号 Inventor C D Label
1 Kenneth W. Kinzler 0.015 458 0.066 849 D3
2 Bert Vogelstein 0.019 288 0.069 034 D3
3 Xu Hong 0.078 413 0.008 913 D2
4 Li Xiaodong 0.094 061 0.003 644 D2
5 Robert Dicosimo 0.044 878 0.042 104 D2
Table 3  明星发明人创新类型标签标注示例
Fig.4  特征词数对识别结果的影响
Fig.5  明星发明人创新类型的早期识别模型参数调优实验
预测模型 A c c/% D1(复合型) D2(巩固型) D3(突破型) D4(发展型)
P/% R/% F 1/% P/% R/% F 1/% P/% R/% F 1/% P/% R/% F 1/%
未嵌入合作关系 64.1 65.8 71.4 68.5 66.7 18.2 28.6 44.4 33.3 38.1 66.7 88.3 73.7
词向量+合作关系 79.4 85.3 82.9 84.1 80.0 72.7 76.2 62.5 41.7 50.0 75.0 88.2 81.1
Table 4  明星发明人创新类型的早期识别研究对比结果
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