Please wait a minute...
Advanced Search
现代图书情报技术  2015, Vol. 31 Issue (9): 52-59     https://doi.org/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
全文: PDF (611 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
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。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
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      出版日期: 2016-04-06
:  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, 2015, 31(9): 52-59.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.09.08      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2015/V31/I9/52

[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.

[1] 陈杰,马静,李晓峰. 融合预训练模型文本特征的短文本分类方法*[J]. 数据分析与知识发现, 2021, 5(9): 21-30.
[2] 李文娜,张智雄. 基于置信学习的知识库错误检测方法研究*[J]. 数据分析与知识发现, 2021, 5(9): 1-9.
[3] 孙羽, 裘江南. 基于网络分析和文本挖掘的意见领袖影响力研究 [J]. 数据分析与知识发现, 0, (): 1-.
[4] 王勤洁, 秦春秀, 马续补, 刘怀亮, 徐存真. 基于作者偏好和异构信息网络的科技文献推荐方法研究*[J]. 数据分析与知识发现, 2021, 5(8): 54-64.
[5] 李文娜, 张智雄. 基于联合语义表示的不同知识库中的实体对齐方法研究*[J]. 数据分析与知识发现, 2021, 5(7): 1-9.
[6] 王昊, 林克柔, 孟镇, 李心蕾. 文本表示及其特征生成对法律判决书中多类型实体识别的影响分析[J]. 数据分析与知识发现, 2021, 5(7): 10-25.
[7] 杨晗迅, 周德群, 马静, 罗永聪. 基于不确定性损失函数和任务层级注意力机制的多任务谣言检测研究*[J]. 数据分析与知识发现, 2021, 5(7): 101-110.
[8] 徐月梅, 王子厚, 吴子歆. 一种基于CNN-BiLSTM多特征融合的股票走势预测模型*[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[9] 黄名选,蒋曹清,卢守东. 基于词嵌入与扩展词交集的查询扩展*[J]. 数据分析与知识发现, 2021, 5(6): 115-125.
[10] 王晰巍,贾若男,韦雅楠,张柳. 多维度社交网络舆情用户群体聚类分析方法研究*[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
[11] 阮小芸,廖健斌,李祥,杨阳,李岱峰. 基于人才知识图谱推理的强化学习可解释推荐研究*[J]. 数据分析与知识发现, 2021, 5(6): 36-50.
[12] 刘彤,刘琛,倪维健. 多层次数据增强的半监督中文情感分析方法*[J]. 数据分析与知识发现, 2021, 5(5): 51-58.
[13] 陈文杰,文奕,杨宁. 基于节点向量表示的模糊重叠社区划分算法*[J]. 数据分析与知识发现, 2021, 5(5): 41-50.
[14] 张国标,李洁. 融合多模态内容语义一致性的社交媒体虚假新闻检测*[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
[15] 闫强,张笑妍,周思敏. 基于义原相似度的关键词抽取方法 *[J]. 数据分析与知识发现, 2021, 5(4): 80-89.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn