知识网络视角下科学、技术、产业间创新驱动关系识别方法研究

Identification Method of Innovation Driving Relationships among Science,Technology,and Industry from the Perspective of the Knowledge Network

开展科学、技术、产业之间创新驱动关系研究,对于实现科学发现、技术创新与产业的深度有效融合与发展具有重要意义。本文从知识网络视角展开科学、技术、产业间创新驱动关系识别方法研究。首先,通过构建时效知识网络,利用社区网络结构熵来表征知识网络结构特征;其次,利用网络结构特征信息间的传递熵探究创新过程中科学、技术、产业三个系统相互驱动的关系;再其次,采用互信息探测主要驱动关系下知识传递路径的变化特点;最后,以再生医学(干细胞)领域作为实证对象,实证研究发现不同时间阶段,科学、技术、产业之间的相互驱动关系会发生转变,三者知识传递路径上知识内容前后继承关系也不同,科学驱动技术过程中的知识传递路径继承性特征持续增强,而技术驱动科学过程中的知识传递路径继承性呈现不稳定、非连续性特征。此外,通过多种方式论证了本文方法的科学有效性,可以用于其他创新领域科学、技术、产业间创新驱动关系的识别。

Research on the relationship between innovation drivers in science,technology,and industry is crucial for achieving deep and effective integration and development of scientific discovery,technological innovation,and industry.This study investigates innovation-driven relationships between science,technology,and industry through the lens of knowledge networks.First,a temporal knowledge network is constructed to capture the structural characteristics of the knowledge network using the concept of structural entropy in the community network.Subsequently,this study explores the relationship between the three systems of science,technology,and industry in the innovation process by analyzing the transfer entropy between the characteristic information of the network structure.Furthermore,the changing characteristics of the knowledge transfer path under the main driving relationship are examined using mutual information.Finally,empirical research focuses on the field of regenerative medicine(stem cells)to investigate how the mutual driving relationships among science,technology,and industry vary at different stages.This study also reveals different inheritance patterns of knowledge content on the knowledge transmission path among the three systems.In science-driven technology processes,the inheritance characteristics of the knowledge transfer path are consistently strengthened.However,in technology-driven science,the inheritance of the knowledge-transfer path is unstable and exhibits discontinuous characteristics.Moreover,this study demonstrates the scientific validity of the proposed method through various means and suggests its applicability in identifying innovation-driven relationships among science,technology,and industry in other innovation fields.