基于研究主题的学科领域知识演化路径识别——以图书情报领域粗糙集为例
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摘要
[目的/意义]对学科领域知识演化路径进行可视化研究,可以帮助研究人员快速发现学科领域中的核心文献和关键主题,把握研究主题的演变趋势。[方法/过程]文章将主路径分析方法与文本挖掘技术相结合,以图书情报(ISLS)领域的粗糙集研究方向为例,识别其核心文献,同时基于向量空间模型对核心文献进行补充,并利用主题模型提取主题,继而绘制知识演化路径图。[结果/结论]研究结果表明:知识演化路径能够全面、细致地展示学科领域的知识内容。该路径不仅能够展示学科领域的不同研究主题、热点主题、核心文献间的关联、研究主题和研究方法的演化趋势,还能够呈现学科领域研究的跨学科特征。
[Purpose/significance]Visualizing the evolutionary path of knowledge in disciplinary fields can help researchers quickly identify core articles,key topics and grasp the evolutionary trends of topics in disciplinary fields.[Method/process]This study combines the main path analysis method and text mining technology,takes the research direction of rough set in the field of Information Science and Library Science(ISLS) as an example,identifies core articles and supplements them based on the vector space model,and then,uses the topic model to extract the topics and draw the knowledge evolution path map.[Result/conclusion]The results show that the knowledge evolution path can comprehensively and meticulously display the knowledge in disciplinary fields.Not only the path can display research topics,hot topics,links between core articles,evolutionary trends of research topics and methods,but also present interdisciplinary features of subject research.
[Purpose/significance]Visualizing the evolutionary path of knowledge in disciplinary fields can help researchers quickly identify core articles,key topics and grasp the evolutionary trends of topics in disciplinary fields.[Method/process]This study combines the main path analysis method and text mining technology,takes the research direction of rough set in the field of Information Science and Library Science(ISLS) as an example,identifies core articles and supplements them based on the vector space model,and then,uses the topic model to extract the topics and draw the knowledge evolution path map.[Result/conclusion]The results show that the knowledge evolution path can comprehensively and meticulously display the knowledge in disciplinary fields.Not only the path can display research topics,hot topics,links between core articles,evolutionary trends of research topics and methods,but also present interdisciplinary features of subject research.
引文
[1] 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(5):101-109.
[2] 韩毅,童迎,夏慧.领域演化结构识别的主路径方法与高被引论文方法对比研究[J].图书情报工作,2013,57(3):11-16.
[3] HUMMON N P,DEREIAN P.Connectivity in a citation network:the development of DNA theory[J].Social Networks,1989,11(1):39-63.
[4] LIU J S,LU L Y Y,LU W M,et al.A survey of DEA applications[J].Omega-international Journal of Management Science,2013,41(5):893-902.
[5] XIAO Y,LU L Y Y,LIU J S,et al.Knowledge diffusion path analysis of data quality literature:a main path analysis[J].Journal of Informetrics,2014,8(3):594-605.
[6] HSIAO C H,TANG K Y,LIU J S.Citation-based analysis of literature:a case study of technology acceptance research[J].Scientometrics,2015(105):1091-1110.
[7] 王晓红,梁玉芳,王福.基于引文网络主路径的移动图书馆研究脉络演化[J].现代情报,2018(6).
[8] 韩毅.引文网络主路径的结构洞功能探索——以知识管理领域为例[J].图书情报工作,2012,56(24):65-70.
[9] 章小童,阮建海.引文网络主路径分析法演化脉络及研究现状的文献计量分析[J].情报资料工作,2016(5).
[10] 钟伟金,李佳,杨兴菊.共词分析法研究(三)——共词聚类分析法的原理与特点[J].情报杂志,2008,27(7):118-120.
[11] MORRIS S A,YEN G,WU Z,et al.Time line visualization of research fronts[J].Journal of the Association for Information Science & Technology,2010,54(5):413-422.
[12] ZHU M,ZHANG X,WANG H.A LDA based model for topic evolution:evidence from information science journals[C]]//International Conference on Modeling.Simulation and Optimization Technologies and Applications,2017:246-249.
[13] WU C C.Constructing a weighted keyword-based patent network approach to identify technological trends and evolution in a field of green energy:a case of biofuels[J].Quality & Quantity,2016,50(1):213-235.
[14] 刘艳华,钱爱兵.21世纪以来国际图书情报领域研究热点的演化路径探析[J].西南民族大学学报:人文社科版,2018(5):229-235.
[15] 刘自强,岳丽欣,王效岳,等.主题演化视角下的国际情报学研究热点与前沿分析[J].图书馆,2017(3):14-22.
[16] 金晨,谢振平,任立园,等.基于时空域联合建模的领域知识演化脉络分析[J].智能系统学报,2017,12(5):735-744.
[17] LIU J S,LU L Y Y.An integrated approach for main path analysis:development of the hirsch index as an example[J].Journal of the American Society for Information Science & Technology,2014,63(3):528-542.
[18] VERSPAGEN B.Mapping technological trajectories as patent citation networks:a study on the history of fuel cell research[J].Advances in Complex Systems,2007(1):93-115.
[19] LEYDESDORFF L,COMINS J A,SORENSEN A A,et al.Cited references and Medical Subject Headings (MeSH) as two different knowledge representations:clustering and mappings at the paper level[J].Scientometrics,2016,109(3):2077-2091.
[20] NOOY W D,MRVAR A,BATAGELJ V.Exploratory social network analysis with Pajek[M]//Exploratory social network analysis with Pajek.Cambridge University Press,2005:605-606.
[21] JGibb LDA-v.1.0[EB/OL].[2018-07-11].https://sourceforge.net/projects/jgibblda/.
[22] 李秀霞,邵作运.融入内容信息的作者共被引分析——以学科服务研究主题为例[J].图书情报工作,2016(1):98-104.
[2] 韩毅,童迎,夏慧.领域演化结构识别的主路径方法与高被引论文方法对比研究[J].图书情报工作,2013,57(3):11-16.
[3] HUMMON N P,DEREIAN P.Connectivity in a citation network:the development of DNA theory[J].Social Networks,1989,11(1):39-63.
[4] LIU J S,LU L Y Y,LU W M,et al.A survey of DEA applications[J].Omega-international Journal of Management Science,2013,41(5):893-902.
[5] XIAO Y,LU L Y Y,LIU J S,et al.Knowledge diffusion path analysis of data quality literature:a main path analysis[J].Journal of Informetrics,2014,8(3):594-605.
[6] HSIAO C H,TANG K Y,LIU J S.Citation-based analysis of literature:a case study of technology acceptance research[J].Scientometrics,2015(105):1091-1110.
[7] 王晓红,梁玉芳,王福.基于引文网络主路径的移动图书馆研究脉络演化[J].现代情报,2018(6).
[8] 韩毅.引文网络主路径的结构洞功能探索——以知识管理领域为例[J].图书情报工作,2012,56(24):65-70.
[9] 章小童,阮建海.引文网络主路径分析法演化脉络及研究现状的文献计量分析[J].情报资料工作,2016(5).
[10] 钟伟金,李佳,杨兴菊.共词分析法研究(三)——共词聚类分析法的原理与特点[J].情报杂志,2008,27(7):118-120.
[11] MORRIS S A,YEN G,WU Z,et al.Time line visualization of research fronts[J].Journal of the Association for Information Science & Technology,2010,54(5):413-422.
[12] ZHU M,ZHANG X,WANG H.A LDA based model for topic evolution:evidence from information science journals[C]]//International Conference on Modeling.Simulation and Optimization Technologies and Applications,2017:246-249.
[13] WU C C.Constructing a weighted keyword-based patent network approach to identify technological trends and evolution in a field of green energy:a case of biofuels[J].Quality & Quantity,2016,50(1):213-235.
[14] 刘艳华,钱爱兵.21世纪以来国际图书情报领域研究热点的演化路径探析[J].西南民族大学学报:人文社科版,2018(5):229-235.
[15] 刘自强,岳丽欣,王效岳,等.主题演化视角下的国际情报学研究热点与前沿分析[J].图书馆,2017(3):14-22.
[16] 金晨,谢振平,任立园,等.基于时空域联合建模的领域知识演化脉络分析[J].智能系统学报,2017,12(5):735-744.
[17] LIU J S,LU L Y Y.An integrated approach for main path analysis:development of the hirsch index as an example[J].Journal of the American Society for Information Science & Technology,2014,63(3):528-542.
[18] VERSPAGEN B.Mapping technological trajectories as patent citation networks:a study on the history of fuel cell research[J].Advances in Complex Systems,2007(1):93-115.
[19] LEYDESDORFF L,COMINS J A,SORENSEN A A,et al.Cited references and Medical Subject Headings (MeSH) as two different knowledge representations:clustering and mappings at the paper level[J].Scientometrics,2016,109(3):2077-2091.
[20] NOOY W D,MRVAR A,BATAGELJ V.Exploratory social network analysis with Pajek[M]//Exploratory social network analysis with Pajek.Cambridge University Press,2005:605-606.
[21] JGibb LDA-v.1.0[EB/OL].[2018-07-11].https://sourceforge.net/projects/jgibblda/.
[22] 李秀霞,邵作运.融入内容信息的作者共被引分析——以学科服务研究主题为例[J].图书情报工作,2016(1):98-104.