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。
[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.
作者
刘向
刘香
余博文
Liu Xiang;Liu Xiang;Yu Bowen(School of Information Management,Central China Normal University,Wuhan 430079,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2023年第2期119-128,共10页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金项目(项目编号:71673106)的研究成果之一。
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
Star Inventors
Duality of Innovation
Early Recognition
Breakthrough Innovation
Continuous Innovation
Relations of Cooperation
作者简介
通讯作者:刘向,ORCID:0000-0003-4315-2699,E-mail:xiangliu@mail.ccnu.edu.cn。
