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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (1): 16-25    DOI: 10.11925/infotech.2096-3467.2017.01.03
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Integrated Analysis and Visualization of Sci-Tech Roadmaps: Case Study of Renewable Energy
Xie Xiufang1,2(), Zhang Xiaolin1
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2School of Health Management and Education, Capital Medical University/Library of Capital Medical University,Beijing 100069, China
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Abstract  

[Objective]This study aims to predict the development trends of science and technology (S&T) with knowledge extracted from S&T roadmaps (STR). [Methods] First, we constructed an STR information database based on the “extraction - synchronization - classification” method of text mining. Second, we analysed the demands and trends of global S&T progress. Finally, we compared and analyzed different countries’ S&T strategies in the field of renewable energy. [Results] We used open source tools, such as Timeflow, Gephi to visualize the results of this case study, such as the globle development trends and national strategic planning in the field of renewable energy by 2050. [Limitations] The automation and personalization features of this study need to be improved. [Conclusions] The proposed method could retrieve strategic intelligence from the STRs effectively.
Key words： Science and Technology Roadmap      Strategic Intelligence      Text Mining      Knowledge Discovery      Integrated Analysis      Information Visualization     
Received: 30 September 2016      Published: 22 February 2017
ZTFLH:  G356.4  
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Xiufang Xie
Xiaolin Zhang

Cite this article:

Xie Xiufang,Zhang Xiaolin. Integrated Analysis and Visualization of Sci-Tech Roadmaps: Case Study of Renewable Energy. Data Analysis and Knowledge Discovery, 2017, 1(1): 16-25.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.01.03     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I1/16

规则 描述 操作
计算符号(+/-)的判定规则(决定在时间t
的基础上执行“加”或“减”操作)		 过去时间 over/past/previous/recent/preceding/last等, 符号取“-”
将来时间 next/following/coming/later等, 符号取“+”
计算加数或减数的取值规则(在时间t
的基础上执行加减的数值n)		 单数 year: n=1; decade: n=10; century: n=100
two/three/ten/20等后接复数 years: 取相应数字, 即n=2/3/10/20等
decades: 取相应数字乘以10, 即n=10×(2/3/10/20)等
centuries: 取相应数字乘以100, 即n=100×(2/3/10/20)等
few/several/some等后接复数 years: n=5; decades: n=50; centuries: n=500
条件 赋值
当前时间(如today/present/current/now) 赋值为发表年份t
beginning/early/dawn/start of century/decade 赋值为该10年或世纪开始的年份
end/late of decade/century 赋值为该10年或世纪结束的年份
时间词为空(不包含且未能继承时间信息) Classification_1=“today” 即属于现状, 赋予发表年份t
Classification_1=“vision” 即属于愿景, 赋予最后一个规划节点年份
Classification_1=“pathway”/“action” 即属于路线或措施, 赋予整个规划区间
Classification_1=“other” 即属于其它, 赋予t-1, 作为其它背景信息
功能目标 参数设置 公共配置
发展愿景
分析		 Classification_1=“vision”;
Classification_2=“target”;		 x轴: time;
y轴: Classification_3;
发展路线
分析		 Classification_1=“pathway”;
Classification_2=“target”;		 权重: Weight;
颜色: Classification_4;
发展战略
分析		 Classification_1=“action”;
Classification_2=“target &
need & barrier & enabler”;		 节点: Keyword;
详情: 鼠标悬停节点显
示原句及所属全部信息
字段;
[1] 刘细文, 柯春晓.技术路线图的应用研究及其对战略情报研究的启示[J]. 图书情报工作, 2007, 51(6): 37-40, 112.
[1] (Liu Xiwen, Ke Chunxiao.The Applications of Technology Roadmap and Its Enlightenment to Strategic Intelligence Research[J]. Library and Information Service, 2007, 51(6): 37-40, 112.)
[2] Zhang Y, Zhang G, Chen H, et al.Topic Analysis and Forecasting for Science, Technology and Innovation: Methodology with a Case Study Focusing on Big Data Research[J]. Technological Forecasting and Social Change, 2016, 105: 179-191.
doi: 10.1016/j.techfore.2016.01.015
[3] Amer M, Daim T U, Jetter A.Technology Roadmap Through Fuzzy Cognitive Map-Based Scenarios: The Case of Wind Energy Sector of a Developing Country[J]. Technology Analysis & Strategic Management, 2016, 28(2): 131-155.
[4] Jin G, Jeong Y, Yoon B.Technology-driven Roadmaps for Identifying New Product/Market Opportunities: Use of Text Mining and Quality Function Deployment[J]. Advanced Engineering Informatics, 2015, 29(1): 126-138.
doi: 10.1016/j.aei.2014.11.001
[5] 叶春蕾, 冷伏海. 技术路线图中未来技术词表构建方法研究[J]. 现代图书情报技术, 2013(5): 59-63.
[5] (Ye Chunlei, Leng Fuhai.Building the Future—Oriented Technology Thesaurus of Technology Roadmap[J]. New Technology of Library and Information Service, 2013(5): 59-63.)
[6] 叶春蕾, 冷伏海. 基于词汇链的路线图关键词抽取方法研究[J]. 现代图书情报技术, 2013(1): 50-56.
[6] (Ye Chunlei, Leng Fuhai.Study on the Keyword Extraction from Roadmap Based on the Lexical Chains[J]. New Technology of Library and Information Service, 2013(1): 50-56.)
[7] Amer M, Daim T U.Application of Technology Roadmaps for Renewable Energy Sector[J]. Technological Forecasting and Social Change, 2010, 77(8): 1355-1370.
doi: 10.1016/j.techfore.2010.05.002
[8] Bader B, Richardson C, Tsuriya M.Technology Roadmap Overviews and Future Direction through Technology Gaps[C]// Proceedings of the 2015 International Conference on Electronics Packaging. 2015.
[9] 谢秀芳, 张晓林. 针对科技路线图的文本挖掘研究框架及特征分析[J]. 情报科学. (待发)..
doi: 10.3969/j.issn.1002-1965.2016.02.009
[9] (Xie Xiufang, Zhang Xiaolin.Text-mining Framework and Feature Analysis on Science and Technology Roadmap [J]. Information Science. In Press.)
doi: 10.3969/j.issn.1002-1965.2016.02.009
[10] 谢秀芳, 张晓林. 针对科技路线图的文本挖掘研究: 信息抽取方法[J]. 情报理论与实践. (待发).
doi: 10.3969/j.issn.1674-1374-B.2002.z1.012
[10] (Xie Xiufang, Zhang Xiaolin.The Research on Text-mining of Science and Technology Roadmap: Method of Information Extraction[J]. Information Studies: Theory & Application. In Press.)
doi: 10.3969/j.issn.1674-1374-B.2002.z1.012
[11] Timeflow [EB/OL]. [2016-07-22]..
[12] 史磊, 王永成. 英文文献自动摘要系统的研制与开发[J]. 高技术通讯, 1999, 9(11): 22-26.
[12] (Shi Lei, Wang Yongcheng.Research and Development of an Automatic Abstracting System for English Documents[J]. Chinese High Technology Letters, 1999, 9(11): 22-26.)
[13] Gephi [EB/OL]. [2016-07-22]..
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