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现代图书情报技术  2015, Vol. 31 Issue (9): 52-59    DOI: 10.11925/infotech.1003-3513.2015.09.08
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
面向单篇文献引文网络的主题来源与走向追踪
秦晓慧1,2, 乐小虬1
1 中国科学院文献情报中心 北京 100190;
2 中国科学院大学 北京 100049
Topic Sources and Trends Tracking Towards Citation Network of Single Paper
Qin Xiaohui1,2, Le Xiaoqiu1
1 National Science Library, Chinese Academy of Sciences, Beijing 100190, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China
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摘要 

[目的]从单篇文献入手, 在其引文网络中追踪研究主题的来源与走向。[方法]首先, 利用领域本体识别单篇文献中的主题; 其次, 筛选与主题相关的二级参考文献、参考文献、引证文献、二级引证文献, 构建面向单篇文献的引文网络; 然后, 对引文网络进行增量聚类处理, 形成主题的来源与走向演化图。[结果]充分揭示文献主题来源或走向中继承、分化、合并的结构变化及各阶段的内容变化。[局限]引文网络构建时文献的筛选条件有待深入研究; 主题识别未考虑领域本体中词汇收录不完备问题。[结论]本研究对单篇文献主题的来源与走向进行有效的追踪, 能够较好地揭示文献主题的来龙去脉。

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Abstract

[Objective] To track topic sources and trends for a high-impact paper from its citation network. [Methods] Firstly, topics are detected for each paper by domain Ontology. Secondly, a citation network towards a single paper's topic is constructed. The nodes of the network are selected from second level cited papers, cited papers, citing papers and second level citing papers according to their contents. Thirdly, incremental cluster is applied for mining topic sources and trends from the network constructed before, the noisy sources or trends are filtered, and evolution paths of topics are formed. [Results] The structure changes and content changes of topic sources and trends are fully revealed. [Limitations] The screening conditions for the construction of citation network need to be further studied. Besides, completeness of the domain Ontology is not considered. [Conclusions] This study tracks topic sources and trends for single paper effectively, and helps reveal origin and development of the topics.

收稿日期: 2015-01-29     
:  TP393  
基金资助:

本文系“十二五”国家科技支撑计划子课题“基于文献知识网络的领域学术关系研究与示范”(项目编号:2011BAH10B06-04)的研究成果之一。

通讯作者: 秦晓慧, ORCID: 0000-0002-3084-2546, E-mail: qinxh@mail.las.ac.cn。     E-mail: qinxh@mail.las.ac.cn
作者简介: 作者贡献声明:秦晓慧:文献调研,细化研究方向及技术方法路线,设计实验方案,数据采集、清洗与结构化,编程及实验结果分析,论文撰写与最终版本修订;乐小虬:提出研究方向和思路,提出研究方案及技术路线,修改论文部分章节,论文审阅及定稿。
引用本文:   
秦晓慧, 乐小虬. 面向单篇文献引文网络的主题来源与走向追踪[J]. 现代图书情报技术, 2015, 31(9): 52-59.
Qin Xiaohui, Le Xiaoqiu. Topic Sources and Trends Tracking Towards Citation Network of Single Paper. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2015.09.08.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.09.08

[1] Web of Science [DB/OL]. [2015-01-27]. http://www.webof­knowledge.com.
[2] 中国知网 [EB/OL]. [2015-01-27]. http://www.cnki.net/. (China National Knowledge Infrastructure [EB/OL]. [2015-01-27]. http://www.cnki.net/.)
[3] Google Scholar [DB/OL]. [2015-01-27]. http://scholar.google.com.sci-hub.org.
[4] Rajaraman K, Tan A. Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks [C]. In: Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'01). London: Springer-Verlag, 2001: 102-107.
[5] 马费成, 张勤. 国内外知识管理研究热点——基于词频的统计分析[J]. 情报学报, 2006, 25(2): 163-171. (Ma Feicheng, Zhang Qin. Comparative Analysis of Knowledge Management Literature Between China and Overseas: A Bibliometric Analysis [J]. Journal of the China Society for Scientific and Technical Information, 2006, 25(2): 163-171.)
[6] 叶春蕾, 冷伏海. 基于共词分析的学科主题演化方法改进研究[J]. 情报理论与实践, 2012, 35(3): 79-82. (Ye Chunlei, Leng Fuhai. Improved Disciplinary Theme Evolution Research Based on Co-word Analysis [J]. Information Studies: Theory & Application, 2012, 35(3): 79-82.)
[7] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation [J]. The Journal of Machine Learning Research, 2003, 3: 993-1022.
[8] 范云满, 马建霞. 利用LDA的领域新兴主题探测技术综述[J]. 现代图书情报技术, 2012(12): 58-65. (Fan Yunman, Ma Jianxia. Review on the LDA-based Techniques Detection for the Field Emerging Topic [J]. New Technology of Library and Information Service, 2012(12): 58-65.)
[9] 贺亮, 李芳. 科技文献话题演化研究[J].现代图书情报技术, 2012(4): 61-67. (He Liang, Li Fang. Topic Evolution in Scientific Literature [J]. New Technology of Library and Information Service, 2012(4): 61-67.)
[10] Morris S A, Yen G, Wu Z, et al. Time Line Visualization of Research Fronts [J]. Journal of American Society for Information Science and Technology, 2003, 54(5): 413-422.
[11] Takeda Y, Kajikawa Y. Optics: A Bibliometric Approach to Detect Emerging Research Domains and Intellectual Bases[J]. Scientometrics, 2009, 78(3): 543-558.
[12] 刘倩楠. 基于专利引文网络的技术演进路径识别研究[D]. 大连: 大连理工大学, 2010. (Liu Qiannan. Research on Identification of Technological Evolution Path Based on Patent Citation Network—Taking Ethemet Technology as an Example [D]. Dalian: Dalian University of Technology, 2010. )
[13] Kegler M C, Rigler J, Ravani M K. Using Network Analysis to Assess the Evolution of Organizations Collaboration in Response to a Major Environmental Health Threat[J]. Health Education Research, 2010, 25(3): 413-424.
[14] Chang S, Chang S, Guh W. Exploring the Technology Diffusion Trajectories and Groups of Basic Patents of Business Methods: Using the Patent Citation Network[C]. In: Proceedings of Portland International Center for Management of Engineering and Technology, Portland. IEEE, 2007: 1784-1789.
[15] 祝清松, 冷伏海. 基于引文内容分析的高被引论文主题识别研究[J]. 中国图书馆学报, 2014, 40(1): 39-49. (Zhu Qingsong, Leng Fuhai. Topic Identification of Highly Cited Papers Based on Citation Content Analysis[J]. Journal of Library Science in China, 2014, 40(1): 39-49.)
[16] 车海燕. 面向中文自然语言Web文档的自动知识抽取和知识融合[D]. 长春: 吉林大学, 2008. (Che Haiyan. Automatic Knowledge Extraction from the Chinese Natural Language Web Documents and Knowledge Consolidation [D]. Changchun: Jilin University, 2008.)
[17] Fukumoto F, Yamaji Y. Topic Tracking Based on Linguistic Features [C]. In: Proceedings of the 2nd International Joint Conference on Natural Language Processing (IJCNLP'05). Heidelberg, Berlin: Springer-Verlag, 2005: 10-21.
[18] Hsu C, Huang Y. Incremental Clustering of Mixed Data Based on Distance Hierarchy [J]. Expert Systems with Applications, 2008, 35(3): 1177-1185.
[19] National Cancer Institute [EB/OL]. [2015-01-14]. http://ncim.nci.nih.gov/ncimbrowser/pages/source_hierarchy.jsf?&sab=NCI.
[20] Motzer R J, Michaelson M D, Redman B G, et al. Activity of SU11248, A Multitargeted Inhibitor of Vascular Endothelial Growth Factor Receptor and Platelet-derived Growth Factor Receptor, in Patients with Metastatic Renal Cell Carcinoma [J]. Journal of Clinical Oncology, 2008, 24(1): 16-24.

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