Please wait a minute...
New Technology of Library and Information Service  2012, Vol. 28 Issue (7): 96-102    DOI: 10.11925/infotech.1003-3513.2012.07.15
Current Issue | Archive | Adv Search |
The Mining Research of Technical Efficiency and Application Map of Patent Information
Zhai Dongsheng, Chen Chen, Zhang Jie, Huang Lucheng, Ruan Pingnan
School of Economics and Management, Beijing University of Technology, Beijing 100124, China
Download: PDF(794 KB)   HTML  
Export: BibTeX | EndNote (RIS)      
Abstract  Currently, the research of patent technology effect matrix and technology application map is primarily depend on the judgement of experts in classification of technology, effect and application terms, which requires many labors and the result seems to be not comprehensive. Therefore,the paper proposes a relatively objective solution based on text mining. This solution mainly focuses on the extraction of patent information about technology, effect and application terms, and then the experts make decisions based on the results of extraction to define the relatively comprehensive and accurate terms. With these terms, the maps by calculating the numbers of terms and the times of occurrences among them can be built. Experimental results show that the finally statistics are more comprehensive relative to experts’ ways.
Key wordsPatent information      Text mining      Technical-efficiency matrix      Technology-application matrix     
Received: 11 June 2012      Published: 11 October 2012



Cite this article:

Zhai Dongsheng, Chen Chen, Zhang Jie, Huang Lucheng, Ruan Pingnan. The Mining Research of Technical Efficiency and Application Map of Patent Information. New Technology of Library and Information Service, 2012, 28(7): 96-102.

URL:     OR

[1] 张伟琼.专利信息采集及分析系统设计与开发[D].杭州:浙江大学, 2008.(Zhang Weiqiong. Design and Develop of Patent Extration and Analysis System[D].Hangzhou:Zhejiang University, 2008.)

[2] 张静, 刘细文, 柯贤能, 等. 国内外专利分析工具功能比较研究[J]. 情报理论与实践 , 2008, 31(1):141-145.(Zhang Jing, Liu Xiwen, Ke Xianneng, et al. Comparative Study of Patent Information Analysis Software Tools at Home and Abroad[J]. Information Studies:Theory & Application, 2008, 31(1):141-145.)

[3] Seol H, Lee S, Kim C. Identifying New Business Areas Using Patent Information:A DEA and Text Mining approach[J]. Expert Systems with Applications, 2011,38(4):2933-2941.

[4] Jun S, Park S S, Jang D S. Technology Forecasting Using Matrix Map and Patent Clustering[J]. Industrial Management & Data Systems, 2012, 112(5):786 - 807.

[5] Cheng T Y. A New Method of Creating Technology/Function Matrix for Systematic Innovation without Expert[J]. Journal of Technology Management & Innovation, 2012, 7(1):18-27.

[6] Tseng Y H, Wang Y M, Juang D W, et al. Text Mining for Patent Map Analysis[C]. In:Proceedings of IACIS Pacific 2005 Conference, Taipei. 2005.

[7] Nanba H, Fujii A, Iwayama M, et al.Overview of the Patent Mining Task at the NTCIR-8 Workshop[C]. In:Proceedings of NTCIR-8 Workshop Meeting, Tokyo. 2010.

[8] 罗立国.基于专利信息服务平台的专利地图研究[D].武汉:华中科技大学, 2009.(Luo Liguo. Patent Map Study Based on Patent Information Service Platform[D]. Wuhan:Huazhong University of Science and Technology, 2009.)

[9] 潘雄锋, 张维维, 舒涛. 我国新能源领域专利地图研究[J]. 中国科技论坛 , 2010(4):41-45. (Pan Xiongfeng, Zhang Weiwei, Shu Tao. Research on Patent Map in the Field of New Energy of China[J]. Forum on Science and Technology in China, 2010(4):41-45.)

[10] 陈颖,张晓林. 专利中技术词和功效词识别方法研究[J]. 现代图书情报技术 , 2011(12):24-30. (Chen Ying, Zhang Xiaolin. Study on the Differentiating Method of Technical and Effect Words in Patent[J]. New Technology of Library and Information Service, 2011(12):24-30.)

[11] 还书国, 邱海霞. Web信息抽取的研究[J]. 消费导刊:理论版 , 2008 (12):172. (Huan Shuguo, Qiu Haixia. The Research of Extraction of Web Information[J]. Consume Guide, 2008 (12):172.)

[12] Feldman R, Aumann Y, Finkelstein-Landau M, et al. A Comparative Study of Information Extraction Strategies[C]. In:Proceedings of the 3rd International Conference on Computational Linguistics and Intelligent Text Processing(CICLing’02). London:Springer-Verlag, 2002:349-359.

[13] 郝占刚, 王正欧. 基于潜在语义索引和遗传算法的文本特征提取方法[J]. 情报科学 ,2006,24(1):104-107.(Hao Zhangang, Wang Zheng’ou. The Method of Text Feature Selection Based on LSI and GA[J]. Information Science, 2006,24(1):104-107.)
[1] Yanan Yang,Wenhui Zhao,Jian Zhang,Shen Tan,Beibei Zhang. Visualizing Policy Texts Based on Multi-View Collaboration[J]. 数据分析与知识发现, 2019, 3(6): 30-41.
[2] Mengji Zhang,Wanyu Du,Nan Zheng. Predicting Stock Trends Based on News Events[J]. 数据分析与知识发现, 2019, 3(5): 11-18.
[3] Ning Zhang,Lemin Yin,Lifeng He. Impacts of “Poster-Follower” Sentiment on Stock Market Performance[J]. 数据分析与知识发现, 2018, 2(6): 1-12.
[4] Xinyue Fan,Lei Cui. Using Text Mining to Discover Drug Side Effects: Case Study of PubMed[J]. 数据分析与知识发现, 2018, 2(3): 79-86.
[5] Qiangbing Wang,Chengzhi Zhang. Constructing Users Profiles with Content and Gesture Behaviors[J]. 数据分析与知识发现, 2017, 1(2): 80-86.
[6] Xiufang Xie,Xiaolin Zhang. Integrated Analysis and Visualization of Sci-Tech Roadmaps: Case Study of Renewable Energy[J]. 数据分析与知识发现, 2017, 1(1): 16-25.
[7] Yao Zhaoxu,Ma Jing. Extracting Topic and Opinion from Microblog Posts with New Algorithm[J]. 现代图书情报技术, 2016, 32(7-8): 78-86.
[8] Lan Qiujun,Liu Wenxing,Li Weikang,Hu Xingye. Sentiment Analysis of Financial Forum Textual Message[J]. 现代图书情报技术, 2016, 32(4): 64-71.
[9] Dongsheng Zhai, He Liu, Jie Zhang, Liwei Cai. Managing Patent Semantic Knowledge with Graph Database[J]. 数据分析与知识发现, 2016, 32(12): 66-75.
[10] Qiang Bi, Jian Liu, Yulai Bao. A New Text Clustering Method Based on Semantic Similarity[J]. 数据分析与知识发现, 2016, 32(12): 9-16.
[11] Lin Yuanyuan,Zhan Hongfei,Yu Junhe,Li Changjiang,Zhang Fan. Using Product Reviews to Analyze Sentiment Fluctuation of Consumer[J]. 现代图书情报技术, 2016, 32(11): 44-53.
[12] Zhao Dongxiao,Wang Xiaoyue,Bai Rujiang,Liu Ziqiang. Semantic Text Mining Methodologies for Intelligence Analysis[J]. 现代图书情报技术, 2016, 32(10): 13-24.
[13] Sui Mingshuang,Cui Lei. Extracting Chemical and Disease Named Entities with Multiple-Feature CRF Model[J]. 现代图书情报技术, 2016, 32(10): 91-97.
[14] Ruyi Yang,Dongsu Liu,Hui Li. An Improved Topic Model Integrating Extra-Features[J]. 现代图书情报技术, 2016, 32(1): 48-54.
[15] Wang Ying, Wu Zhenxin, Xie Jing. Review on Semantic Retrieval System for Scientific Literature[J]. 现代图书情报技术, 2015, 31(5): 1-7.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938