中国专利奖视角下的颠覆性技术识别分析

Analysis on Disruptive Technology Identification from the Perspective of China Patent Award

摘要:

为充分利用专利结构化数据和文本数据,实现准确有效的颠覆性技术识别,以中国专利奖为切入点,提出基于图神经网络的颠覆性技术识别框架。首先,选择获得中国专利奖的授权发明专利定义颠覆性技术,解决技术定义难的问题；接着,使用Neo4j图数据库构建异质有向图,存储专利数据构成的多重关系和方向信息,解决专利关系数据利用率低的问题；最后,使用关系图卷积神经网络(R-GCN)模型进行训练,实现颠覆性技术的识别,解决技术识别效果不佳的问题。研究表明获得中国专利奖的专利作为颠覆性技术的直接定义是合理且可靠的；提出的颠覆性技术识别框架是有效的,能够充分利用专利数据信息和图中空间信息,识别出绝大部分的颠覆性技术,丰富了图神经网络在颠覆性技术识别方面的研究。

Abstract:

In order to fully utilize structured and textual patent data and realize accurate and effective disruptive technology identification, the disruptive technology identification framework based on graph neural networks is proposed with the China Patent Award as the breakthrough point. Firstly, to solve the problem of difficult technology definition, disruptive technologies are defined with the authorized invention patents that have won the China Patent Award; Then, the heterogeneous directed graph is constructed by the Neo4j graph database to store patent multiple relationships and directional information addressing the issue of low utilization of relational data; Finally, the Relationship Graph Convolutional Network (R-GCN) model is used to achieve disruptive technology identification for the solution to poor recognition effect. The research has shown that it’s reasonable and reliable to directly define disruptive technologies with the patent which have won the China Patent Award; The proposed disruptive technology identification framework can make full use of patent data information and spatial information to identify the vast majority of disruptive technologies, enriching the research of graph neural networks in the area of disruptive technology identification.