Technology Novelty Assessment Based on Knowledge Reorganization and Variation: Case Study of Digital Medicine
Yang Siluo1,2,Jiang Man1,2(),Gao Qiang3
1School of Information Management, Wuhan University, Wuhan 430072, China 2Research Center for Chinese Science Evaluation, Wuhan University, Wuhan 430072, China 3College of Cyber Security, Shandong University of Political Science and Law, Jinan 250014, China
[Objective] This paper proposes a technical novelty assessment method based on knowledge reorganization and variation as well as the source and formation mechanism of technological novelty. It addresses the problem of using substitute indicators and ignoring the connotation of novelty in technical novelty assessment. [Methods] First, we analyzed the sources of technological novelty at the micro level, which helps us clarify the internal relationship from knowledge units to knowledge reorganization and from variation to technological novelty. Then, we constructed the technical novelty evaluation indexes at three levels: knowledge source diversity, reorganization novelty, and knowledge variation breakthrough degree around two main lines of knowledge reorganization and variation. Finally, we verified the feasibility and effectiveness of the method with digital medical technology. [Results] We identified the technologies with high novelty and their scores. The recall values of this proposed method were about 23.19%, 5.24%, and 9.69% higher than the commonly used citation, cosine similarity, and knowledge diversity methods. [Limitations] More research is needed to explore categorizing knowledge units under different classification schemes. [Conclusions] Knowledge reorganization and variation are two leading reasons for technology innovation. The proposed method can effectively identify technologies of high novelty.
杨思洛, 江曼, 高强. 基于知识重组和变异的技术新颖性评估——以数字医疗技术为例*[J]. 数据分析与知识发现, 2023, 7(12): 52-63.
Yang Siluo, Jiang Man, Gao Qiang. Technology Novelty Assessment Based on Knowledge Reorganization and Variation: Case Study of Digital Medicine. Data Analysis and Knowledge Discovery, 2023, 7(12): 52-63.
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