Library and Information Service >
Multi-dimension Dynamic Evolution Analysis of Technology Topics Based on AToT by Taking Grapheme Technology as an Example
Received date: 2016-10-27
Revised date: 2017-02-22
Online published: 2017-03-05
[Purpose/significance] Multi-dimension evolution analysis based on the AToT model could not only provide thorough insights into the evolution process of technology topics, mastering technological composition trends among industries in different periods, but have advantages in analyzing the technological development process in each tache of the industry chain, laying solid foundation for industrial innovation. [Method/process] This paper revealed the latent technology topics and technological attention of patent-owners through keywords and phrases which were extracted from abstracts in each patent document, and showed deep insight into the technological development status with industry chain information. [Result/conclusion] The experiment turns out that the method provided in this paper could not only analyze the content of patents effectively, but reveal the dynamic evolution process of enterprise technological topics.
Key words： AToT; topic evolution; grapheme
Wu Feifei , Zhang Yaru , Huang Lucheng , Li Xin , Luan Jingjing . Multi-dimension Dynamic Evolution Analysis of Technology Topics Based on AToT by Taking Grapheme Technology as an Example[J]. Library and Information Service, 2017 , 61(5) : 95 -102 . DOI: 10.13266/j.issn.0252-3116.2017.05.013
[1] 张娴, 方曙, 王春华. 专利引证视角下的技术演化研究综述[J]. 科学学与科学技术管理, 2016,37(3):58-67.
[2] 李长玲, 刘非凡, 魏绪秋. 基于3-mode网络的领域主题演化规律分析——以知识网络研究领域为例[J]. 情报理论与实践, 2014, 37(12):104-110.
[3] 张云, 华薇娜, 袁顺波,等. WoS数据库中专利分析论文的主题动态演进研究[J].现代图书情报技术, 2015,31(1):17-23.
[4] BLOCK J H, MILLER D, JASKIEWICZ P, et al. Economic and technological importance of innovations in large family and founder firms:an analysis of patent data[J]. Family business review, 2013, 26(4):487-494.
[5] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of machine learning research, 2003, 3(4):993-1022.
[6] ROSEN-ZVI M, GRIFFITHS T, STEYVERS M, et al. The author-topic model for authors and documents[C]//Proceedings of the 20th conference on uncertainty in artificial intelligence. Virginia:AUAI Press, 2004:487-494.
[7] WANG X, MCCALLUM A. Topics over time:a non-Markov continuous-time model of topical trends[C]//Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. New York:ACM Press, 2006:424-433.
[8] XU S, SHI Q, QIAO X, et al. Author-Topic over Time (AToT):a dynamic users'iInterest model[J]. Lecture notes in electrical engineering, 2014, 274:58-61.
[9] 史庆伟,乔东伟,徐硕.作者主题演化模型以及研究兴趣演化分析中的应用[J].情报学报,2013,32(9):912-919.
[10] CHOI C, PARK Y. Monitoring the organic structure of technology based on the patent development paths[J]. Technological forecasting and social change, 2009, 76(6):754-768.
[11] MARTINELLI A. An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry[J]. Research policy, 2012, 41(2):414-429.
[12] 刘美佳.基于专利分析的RFID技术演化研究[D].北京:北京工业大学,2013.
[13] HAUPT R, KLOYER M, LANGE M. Patent indicators for the technology life cycle development[J]. Research policy, 2007, 36(3):387-398.
[14] 钟华, 邓辉. 基于技术生命周期的专利组合判别研究[J]. 图书情报工作, 2012, 56(18):87-92.
[15] YOON J, KIM K. An automated method for identifying TRIZ evolution trends from patents[J]. Expert systems with applications, 2011, 38(12):15540-15548.
[16] YOUNG G, JONG H, SANG C. Visualization of patent analysis for emerging technology[J].Expert systems with applications, 2008, 34(3):1804-1812.
[17] 方曙,胡正银,庞弘燊,等.基于专利文献的技术演化分析方法研究[J].图书情报工作, 2011, 55(22):42-46.
[18] 王燕鹏.国内基于主题模型的科技文献主题发现及演化研究进展[J].图书情报工作,2016,60(3):130-137.
[19] 祝娜, 王芳. 基于主题关联的知识演化路径识别研究——以3D打印领域为例[J]. 图书情报工作, 2016,60(5):101-109.
[20] 曹丽娜,唐锡晋.基于主题模型的BBS话题演化趋势分析[J].管理科学学报,2014,17(11):109-120.
[21] 廖君华,孙克迎,钟丽霞.一种基于时序主题模型的网络热点话题演化分析系统[J].图书情报工作,2013,57(9):97-102.
[22] 郭红梅, 张智雄. 基于图挖掘的文本主题识别方法研究综述[J]. 中国图书馆学报, 2015,41(6):97-108.
[23] KAPLAN S, VAKILI K. Novelty vs. usefulness in innovative breakthroughs:a test using topic modeling of nanotechnology patents[J] Strategic management journal, 2015,36:1435-1457
[24] VENUGOPALAN S, RAI V. Topic based classification and pattern identification in patents[J]. Technological forecasting and social change, 2015, 94(1):236-250.
[25] WU Q, ZHANG C, HONG Q, et al. Topic evolution based on LDA and HMM and its application in stem cell research[J]. Journal of information science, 2014, 40(5):611-620.
[26] WANG B, LIU S, DING K, et al. Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis:a case study in LTE technology[J]. Scientometrics, 2014, 101(1):685-704.
[27] 戴岳.鲁企试破石墨烯应用"梗阻"[N].经济导报,2016-03-21.
[28] KIM E, CHO Y, KIM W. Dynamic patterns of technological convergence in printed electronics technologies:patent citation network[J]. Scientometrics, 2014, 98(2):975-998.
/
| 〈 |
|
〉 |
