Library and Information Service >
Research on the Discipline Topic Evolution Analysis Method of Semantic Classification——A Case Study of Big Data in the Field of Library and Information Science in China
Received date: 2016-05-23
Revised date: 2016-07-16
Online published: 2016-08-05
[Purpose/significance] Topic evolution research is helpful to master the development situation, research focus, research front and development trend in a discipline. It is an important research direction for science and technology innovation.[Method/process] This paper put forward an analysis method of discipline topic evolution based on semantic classification. First, the authors classified keywords into three types of semantic roles——research problems, research methods and research techniques, then constructed co-keywords network of different semantic roles. After this, based on the fast unfolding community discovery algorithm, the authors detected the semantic features of the community (topic); the similarity algorithm was used to calculate similarity between the topics of adjacent periods. The authors also constructed the topic evolution map to analyze changes in a discipline's research problems, research methods and research techniques and achieve an in-depth and meticulous topic evolution analysis. The accuracy and effectiveness of the method are proved according to the analysis of the data of big data in the related research field from 2012-2015 in CNKI.
Liu Ziqiang , Wang Xiaoyue , Bai Rujiang . Research on the Discipline Topic Evolution Analysis Method of Semantic Classification——A Case Study of Big Data in the Field of Library and Information Science in China[J]. Library and Information Service, 2016 , 60(15) : 76 -85,93 . DOI: 10.13266/j.issn.0252-3116.2016.15.011
[1] 王效岳,白如江.海量网络学术文献自动分类技术研究[M].北京:人民出版社,2015:40-42.
[2] BUSH V.As we may think[EB/OL].[2016-03-26].http://ccat.sas.upenn.edu/~jod/texts/vannevar.bush.html.
[3] KLEINBERG J.Bursty and hierarchical structure in streams[J].Data mining&knowledge discovery,2003,7(4):373-397.
[4] KONTOSTATHIS A,GALITSKY L M,POTTER NGER W M,et al.A survey of emerging trend in textual data mining[M].New York:Springer Verlag,2004:185-224.
[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(2):146-151.
[6] 邱均平,温芳芳.近五年来图书情报学研究热点与前沿的可视化分析——基于13种高影响力外文源刊的计量研究[J].中国图书馆学报,2011(2):51-60.
[7] KOSTOFF R N.EBERHART H J,TOOTHMAN D R,et al.Database tomography for technical intelligence:comparative roadmaps of the research impact assessment literature and the Journal of the american chemical society[J].Scientiometrics,1997,40(1):103-138.
[8] DING Y,CHOWDHURY G G,FOO S.Bibliometric cartography of information retrieval research by using co-word analysis[J].Information processing and management,2001,37(6):817-842.
[9] MANE K K,BORNER K.Mapping topics and topic bursts in PNAS[J].Proceedings of the National Academy of Sciences of the United States of America,2004,101(Suppl 1):5287-5290.
[10] RITZHAUPT A D.An investigation of distance education in North American research literature using co-word analysis[J].International review of research in open&distance learning,2010,11(1):37-60.
[11] 刘晓波.我国图书馆学研究热点及趋势——基于关键词共现和词频统计的可视化研究[J].图书情报工作,2012,56(7):62-67.
[12] GIRVAN M,NEWMAN M E J.Community structure in socia1and biological networks[J].Proceedings of the National Academy of Sciences of the United States of America,2002,99(12):7821-7826.
[13] 杨博,刘大有,LIU J M,等.复杂网络聚类方法[J].软件学报,2009,20(1):54-66.
[14] WALLACE M L,GINGRAS Y,DUHON R.A new approach for detecting scientific specialties from raw cocitation networks[J].Journal of the American Society for Information Science and Technology,2009,60(2):240-246.
[15] 程齐凯,王晓光.一种基于共词网络社区的科研主题演化分析框架[J].图书情报工作,2013,57(8):91-96.
[16] 白如江,冷伏海.k-clique社区知识创新演化方法研究[J].图书情报工作,2013,57(17):94-99.
[17] 刘自强,王效岳,白如江.基于时间序列模型的研究热点分析预测方法研究[J].情报理论与实践,2016,39(5):27-33.
[18] DONOHUE J C.Understanding Scientific Literature[M].Massachusetts:The MIT Press,1974.
[19] NEWMAN M E J,GIRVAN M.Finding and evaluating community structure in networks[J].Physical review,2004,69(2):108-113.
[20] BLONDEL V D,GUILLAUME J L,LAMBIOTTE R,et al.Fast unfolding of communities in large networks[J].Journal of Statistical Mechanics:Theory and Experiment,2008,30(2):155-168.
[21] LIN D.An information theoretic definition of similarity[C].//Fifteenth international conference on machine learning.San Francisco:Morgan Kaufman Publishers,1998:296-304.
/
| 〈 |
|
〉 |
