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| Examining Multi-dimensional Technology Evolution Path and Technology Innovation Opportunity Identification Based on SAO Semantic Analysis |
| Feng Lijie1,4, Zhou Wei1, Wang Jinfeng2, Zhang Ke1, Zhang Shibin3 |
1.School of Management, Zhengzhou University, Zhengzhou 450001
2.China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306
3.School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045
4.Logistics Engineering College, Shanghai Maritime University, Shanghai 201306 |
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Abstract Exploring the evolution path of key technologies in the target field and accurately identifying technological innovation opportunities represent essential steps for enterprises to stimulate technological innovation vitality. This study proposes a multi-dimensional technology evolution path and technology innovation opportunity identification method by combining SAO (subject-action-object) semantic analysis and the multi-dimensional technology innovation map. Firstly, the LDA (latent Dirichlet allocation) algorithm was used to determine the key technical issues in the target area. Secondly, the SAO semantic analysis method was utilized to accurately identify the semantic structure containing key technology elements, and the multi-dimensional technology innovation map was employed to navigate and classify key technology elements. Thereafter, by constructing the multi-dimensional technology evolution path and deeply exploring the law of technology evolution under different dimensions, the iterative transformation with innovation law was conducted to accurately judge the technology innovation opportunities. Finally, the modification technology of nano-TiO2 was taken as an example to conduct the analysis. Finally, the comparative analysis method was used to verify the effectiveness and practicability of the method.
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Received: 09 November 2021
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1 Noh H, Kim K, Song Y K, et al. Opportunity-driven technology roadmapping: the case of 5G mobile services[J]. Technological Forecasting and Social Change, 2021, 163: 120452.
2 陈悦, 王康, 宋超, 等. 一种用于技术融合与演化路径探测的新方法：技术群相似度时序分析法[J]. 情报学报, 2021, 40(6): 565-574.
3 Sasaki H, Sakata I. Identifying potential technological spin-offs using hierarchical information in international patent classification[J]. Technovation, 2021, 100: 102192.
4 李慧, 孟玮. 专利视角下的美国空军核心技术演化分析[J]. 情报理论与实践, 2021, 44(2): 41-49.
5 陈翔, 黄璐, 倪兴兴, 等. 基于动态语义网络分析的主题演化路径识别研究[J]. 情报学报, 2021, 40(5): 500-512.
6 Geum Y, Kim M. How to identify promising chances for technological innovation: keygraph-based patent analysis[J]. Advanced Engineering Informatics, 2020, 46: 101155.
7 Huang Y, Zhu D H, Qian Y, et al. A hybrid method to trace technology evolution pathways: a case study of 3D printing[J]. Scientometrics, 2017, 111(1): 185-204.
8 Chen M C, Ho P H. Exploring technology opportunities and evolution of IoT-related logistics services with text mining[J]. Complex & Intelligent Systems, 2021, 7(5): 2577-2595.
9 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.
10 刘向, 万小萍, 闫肖婷, 等. 基于引文路径叠加网络的主路径分析[J]. 情报学报, 2019, 38(8): 807-814.
11 李乾瑞, 郭俊芳, 朱东华. 新兴技术创新机会识别方法研究[J]. 中国软科学, 2018(11): 138-147.
12 He X J, Meng X, Dong Y B, et al. Demand identification model of potential technology based on SAO structure semantic analysis: the case of new energy and energy saving fields[J]. Technology in Society, 2019, 58: 101116.
13 Yang C, Zhu D H, Wang X F, et al. Requirement-oriented core technological components’ identification based on SAO analysis[J]. Scientometrics, 2017, 112(3): 1229-1248.
14 Kim S, Yoon B. Patent infringement analysis using a text mining technique based on SAO structure[J]. Computers in Industry, 2021, 125: 103379.
15 Kim K, Park K, Lee S. Investigating technology opportunities: the use of SAOx analysis[J]. Scientometrics, 2019, 118(1): 45-70.
16 Choi S, Park H, Kang D, et al. An SAO-based text mining approach to building a technology tree for technology planning[J]. Expert Systems with Applications, 2012, 39(13): 11443-11455.
17 Wang X F, Ren H C, Chen Y, et al. Measuring patent similarity with SAO semantic analysis[J]. Scientometrics, 2019, 121(1): 1-23.
18 岳俊举, 冯立杰, 冯奕程, 等. 基于多维技术创新地图与关联规则挖掘的技术机会识别方法研究[J]. 情报学报, 2017, 36(8): 798-808.
19 冯立杰, 尤鸿宇, 王金凤. 专利技术创新路径识别及其新颖性评价研究[J]. 情报学报, 2021, 40(5): 513-522.
20 冯立杰. 元易创新方法: 技术创新的九维九法[M]. 重庆: 重庆大学出版社, 2020: 18-31.
21 冯立杰, 曹健, 王金凤, 等. 基于FBS和多维技术创新地图的技术创新机会识别方法及其应用[J]. 情报理论与实践, 2020, 43(12): 89-95.
22 冯立杰, 曾小红, 王金凤, 等. 一种三级技术机会识别方法及其应用——基于SAO语义分析和多维技术创新地图[J]. 科技进步与对策, 2021, 38(19): 1-10.
23 陈伟, 林超然, 李金秋, 等. 基于LDA-HMM的专利技术主题演化趋势分析——以船用柴油机技术为例[J]. 情报学报, 2018, 37(7): 732-741.
24 徐戈, 杨晓燕, 汪涛. 单词语义相似性计算综述[J]. 计算机工程与应用, 2020, 56(4): 9-15.
25 Sánchez D, Batet M. A semantic similarity method based on information content exploiting multiple ontologies[J]. Expert Systems with Applications, 2013, 40(4): 1393-1399.
26 何喜军, 马珊, 武玉英. 基于本体和SAO结构的线上技术供需信息语义匹配研究[J]. 情报科学, 2018, 36(11): 95-100.
27 Ma Y Z, Li L L, Wang H, et al. Laboratory study on performance evaluation and automobile exhaust degradation of nano-TiO2 particles-modified asphalt materials[J]. Advances in Materials Science and Engineering, 2021, 2021: Article ID 5574013.
28 Roy K, Sikdar D, Mandal S K, et al. Surface modification of nano titanium dioxide (TiO2) by cationic surfactants and investigation of its effect on the properties of natural rubber (NR) nanocomposites[J]. Rubber Chemistry and Technology, 2020, 93(2): 346-359.
29 任海英, 王倩. 技术机会发现方法的研究现状、趋势和问题[J]. 情报杂志, 2020, 39(4): 51-59.
30 马铭, 王超, 周勇, 等. 基于语义信息的核心技术主题识别与演化趋势分析方法研究[J]. 情报理论与实践, 2021, 44(9): 106-113.
31 王康, 高继平, 潘云涛, 等. 多位态研究主题识别及其演化路径方法研究[J]. 图书情报工作, 2021, 65(11): 113-122.
32 周源, 杜俊飞, 刘宇飞, 等. 基于引用网络和文本挖掘的技术演化路径识别[J]. 情报杂志, 2018, 37(10): 76-81.
33 马俊红, 张文凤, 冯鑫, 等. 克服引文滞后的科技演化主路径测绘[J]. 情报杂志, 2021, 40(5): 186-192.
34 Miao Z Z, Du J F, Dong F, et al. Identifying technology evolution pathways using topic variation detection based on patent data: a case study of 3D printing[J]. Futures, 2020, 118: 102530. |
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