Technology Opportunity Identification Based on Technology Correlation—Taking the Field of High-Temperature Superconductivity as an Example
Zhu Xiangli1,2, Li Qianzhi1,2, Liu Xiaoping1,2
1.National Science Library, Chinese Academy of Sciences, Beijing 100190 2.Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
摘要为提升从科学研究中识别潜在技术机会的精准性,本文提出一种融合语义分析与拓扑建模的科学-技术关联识别框架。结合BERTopic(bidirectional encoder representations from transformers topic modeling)主题建模、显式共词与隐式链路预测等方法,构建多维度科学-技术关联指标,采用TOPSIS-CRITIC(technique for order preference by similarity to ideal solution - criteria importance through intercriteria correlation)模型评估科学主题的创新性与技术发展的制约因素,从而识别出具有高发展潜力的技术方向。以高温超导领域为案例开展实证分析,识别出6个有发展潜力的技术机会,与领域前沿发展方向具有良好的一致性,验证了该方法的有效性与前瞻性。本文的创新点在于提出术语级链路预测方法解决科技术语隔阂问题,探索了融合语义与结构特征识别有发展潜力的技术机会的路径。
朱相丽, 李芊芷, 刘小平. 基于科学-技术关联的技术机会识别——以高温超导领域为例[J]. 情报学报, 2026, 45(2): 230-242.
Zhu Xiangli, Li Qianzhi, Liu Xiaoping. Technology Opportunity Identification Based on Technology Correlation—Taking the Field of High-Temperature Superconductivity as an Example. 情报学报, 2026, 45(2): 230-242.
1 苏娜平, 谭宗颖. 技术机会分析方法研究综述与展望[J]. 情报理论与实践, 2020, 43(11): 179-186. 2 吕薇. 多措并举促进基础研究转化为原始创新能力[J]. 科技中国, 2018(2): 1-5. 3 王诗炜, 陈春. 基于科学论文和技术专利关联关系识别潜在知识发现方法研究综述[J]. 数据分析与知识发现, 2023, 7(7): 18-31. 4 Ogawa T, Kajikawa Y. Assessing the industrial opportunity of academic research with patent relatedness: a case study on polymer electrolyte fuel cells[J]. Technological Forecasting and Social Change, 2015, 90: 469-475. 5 Shibata N, Kajikawa Y, Sakata I. Extracting the commercialization gap between science and technology—Case study of a solar cell[J]. Technological Forecasting and Social Change, 2010, 77(7): 1147-1155. 6 Takano Y, Kajikawa Y. Extracting commercialization opportunities of the Internet of Things: measuring text similarity between papers and patents[J]. Technological Forecasting and Social Change, 2019, 138: 45-68. 7 王坤, 王京安, 汤月, 等. 基于专利和科技论文的技术机会识别研究——以金属3D打印技术为例[J]. 科技管理研究, 2018, 38(7): 73-79. 8 尹航, 李云柯, 王志楠, 等. 科技差距视角下技术机会识别方法优化研究[J]. 科学学研究, 2025, 43(2): 300-310. 9 Song K, Kim K S, Lee S. Discovering new technology opportunities based on patents: text-mining and F-term analysis[J]. Technovation, 2017, 60-61: 1-14. 10 韩晓彤, 朱东华, 汪雪锋. 科学推动下技术机会发现方法研究[J]. 图书情报工作, 2022, 66(10): 19-32. 11 Li M N, Wang W S, Zhou K Y. Exploring the technology emergence related to artificial intelligence: a perspective of coupling analyses[J]. Technological Forecasting and Social Change, 2021, 172: 121064. 12 Ba Z C, Meng K, Ma Y X, et al. Discovering technological opportunities by identifying dynamic structure-coupling patterns and lead-lag distance between science and technology[J]. Technological Forecasting and Social Change, 2024, 200: 123147. 13 Gl?nzel W, Meyer M. Patents cited in the scientific literature: an exploratory study of ‘reverse’ citation relations[J]. Scientometrics, 2003, 58(2): 415-428. 14 Gao J P, Ding K, Teng L, et al. Hybrid documents co-citation analysis: making sense of the interaction between science and technology in technology diffusion[J]. Scientometrics, 2012, 93(2): 459-471. 15 Narin F, Noma E. Is technology becoming science?[J]. Scientometrics, 1985, 7(3): 369-381. 16 高继平, 姚长青, 马峥, 等. 论文专利互引分析方法的应用及其不足——在科学技术关联分析方面[J]. 科学学与科学技术管理, 2014, 35(12): 39-44. 17 宁子晨, 魏来. 专利主体视角下专利文献与学术论文关联关系发现研究——以“数据挖掘”主题为例[J]. 图书情报工作, 2020, 64(12): 106-117. 18 李睿, 容军凤, 张玲玲. 试论“科学-技术关联”计量模型的不足及改进——学科-领域对应优化视角[J]. 图书情报工作, 2013, 57(5): 86-93. 19 董坤, 许海云, 罗瑞, 等. 科学与技术的关系分析研究综述[J]. 情报学报, 2018, 37(6): 642-652. 20 马亚雪, 巴志超, 曹祯庭, 等. 科学-技术关联对高技术产业创新绩效的影响研究——对外技术依存度的调节作用[J]. 情报学报, 2024, 43(7): 839-849. 21 许海云, 王超, 陈亮, 等. 颠覆性技术的科学-技术-产业互动模式识别与分析[J]. 情报学报, 2023, 42(7): 816-831. 22 Ba Z C, Liang Z T. A novel approach to measuring science-technology linkage: from the perspective of knowledge network coupling[J]. Journal of Informetrics, 2021, 15(3): 101167. 23 Xu H Y, Yue Z H, Pang H S, et al. Integrative model for discovering linked topics in science and technology[J]. Journal of Informetrics, 2022, 16(2): 101265. 24 Chen X, Ye P F, Huang L, et al. Exploring science-technology linkages: a deep learning-empowered solution[J]. Information Processing & Management, 2023, 60(2): 103255. 25 Xu H Y, Winnink J, Yue Z H, et al. Topic-linked innovation paths in science and technology[J]. Journal of Informetrics, 2020, 14(2): 101014. 26 Grootendorst M. BERTopic: neural topic modeling with a class-based TF-IDF procedure[OL]. (2022-03-11). https://arxiv.org/pdf/2203.05794. 27 陈靖元. 基于词语权重分析的中文文本相似检测技术研究[D]. 郑州: 郑州大学, 2021. 28 Li X, Xie Q Q, Daim T, et al. Forecasting technology trends using text mining of the gaps between science and technology: the case of perovskite solar cell technology[J]. Technological Forecasting and Social Change, 2019, 146: 432-449. 29 王丽然. 专利技术词与功效词抽取系统的设计与实现[D]. 北京: 首都经济贸易大学, 2022. 30 Carpenter M P, Cooper M, Narin F. Linkage between basic research literature and patents[J]. Research Management, 1980, 23(2): 30-35. 31 Liu Z Q, Shen Y, Cheng X C, et al. Learning representations of inactive users: a cross domain approach with graph neural networks[C]// Proceedings of the 30th ACM International Conference on Information & Knowledge Management. New York: ACM Press, 2021: 3278-3282. 32 Burt R S. Structural holes: the social structure of competition[M]. Cambridge: Harvard University Press, 1992. 33 刘自强, 许海云, 罗瑞, 等. 基于主题关联分析的科技互动模式识别方法研究[J]. 情报学报, 2019, 38(10): 997-1011. 34 Xu H Y, Luo R, Winnink J, et al. A methodology for identifying breakthrough topics using structural entropy[J]. Information Processing & Management, 2022, 59(2): 102862. 35 Min C, Bu Y, Sun J J. Predicting scientific breakthroughs based on knowledge structure variations[J]. Technological Forecasting and Social Change, 2021, 164: 120502. 36 杨思洛, 江曼. 新兴技术内涵特征和识别方法研究进展[J]. 情报科学, 2023, 41(5): 181-190. 37 马亚雪, 王秀, 熊宇宸, 等. 突破性科学创新研究: 内涵、进展与展望[J]. 现代情报, 2025, 45(12): 167-177. 38 李昌, 杨中楷, 董坤. 基于多维属性动态变化特征的新兴技术识别研究[J]. 情报学报, 2022, 41(5): 463-474. 39 魏绪秋, 常霞, 姜召昊, 等. 主题新颖性程度—融合程度—影响力三维视角下的学术论文创新性评价研究[J]. 现代情报, 2024, 44(3): 131-139, 177. 40 黄璐, 朱一鹤, 张嶷. 基于加权网络链路预测的新兴技术主题识别研究[J]. 情报学报, 2019, 38(4): 335-341.