一种基于TDT模型的基金项目科学研究前沿识别方法研究
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摘要
[目的/意义]由于传统以论文分析数据源的科学研究前沿方法存在主题探测的时滞性及方法的局限性,需要更加准确识别出蕴含更多前瞻价值信息的基金项目数据中的研究前沿主题,细粒度识别科学研究前沿主题类型。[方法/过程]提出一种基于TDT模型的基金项目科学研究前沿识别方法,借鉴TDT模型中多要素融合分析及归一化处理的思想,分析基金项目的资助强度、时间维度、主题维度等项目特征属性,构建基金项目科学研究前沿探测公式,识别出热门研究前沿主题、新兴前沿主题及未来前沿主题3种科学研究前沿主题,从而揭示前沿领域竞争态势。[结果/结论]通过以石墨烯领域数据进行实验,表明该方法能够更加快速准确地前瞻识别出科学研究前沿主题,弥补单一主题维度进行前沿识别的不足,更好地进行主题发展预测分析。
[Purpose/significance] Traditional scientific research fronts detection methods taking analytic data of papers as data sources have time lag in topic identification and limited methods. This paper aims to detect the topics of research fronts in fund sponsored projects more accurately,which contains more prospective and valuable information, and to refine the topic types.[Method/process] The scientific research fronts detection methods of fund sponsored projects based on the TDT model is proposed. Based on the multi-factor integration analysis and normalization idea in TDT model,the paper analyzes the funding intensity,time dimension,topic dimension,and other attributes of fund sponsored projects,constructs the formula of scientific research fronts detection,and identifies the hot research front topics,the emerging front topics and future front topics,so as to reveal the competitive situation of scientific research fronts. [Result/conclusion]Through the experimental data in the field of graphene,the results show that this method can identify topics of scientific research fronts more quickly and accurately,which makes up the limitations of fronts detection within a single dimension,and can better predict and analyze topic development.
[Purpose/significance] Traditional scientific research fronts detection methods taking analytic data of papers as data sources have time lag in topic identification and limited methods. This paper aims to detect the topics of research fronts in fund sponsored projects more accurately,which contains more prospective and valuable information, and to refine the topic types.[Method/process] The scientific research fronts detection methods of fund sponsored projects based on the TDT model is proposed. Based on the multi-factor integration analysis and normalization idea in TDT model,the paper analyzes the funding intensity,time dimension,topic dimension,and other attributes of fund sponsored projects,constructs the formula of scientific research fronts detection,and identifies the hot research front topics,the emerging front topics and future front topics,so as to reveal the competitive situation of scientific research fronts. [Result/conclusion]Through the experimental data in the field of graphene,the results show that this method can identify topics of scientific research fronts more quickly and accurately,which makes up the limitations of fronts detection within a single dimension,and can better predict and analyze topic development.
引文
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[13]梁伟波.美国NSF资助物流项目的知识图谱分析[J].情报杂志,2016,35(10):114-119.
[14]洪宇,张宇,刘挺,等.话题检测与跟踪的评测及研究综述[J].中文信息学报,2007,21(6):71-87.
[15]PAPKA R,ALLAN J.Topic detection and tracking:event clustering as a basis for first story detection[J].Information Retrieval,2000,7:97-126.
[16]LAM W,MENG H M L,WONG K L,et al.Using contextual analysis for news event detection[J].International Journal of Intelligent Systems,2010,16(4):525-546.
[17]范云满,马建霞.基于LDA与新兴主题特征分析的新兴主题探测研究[J].情报学报,2014,33(7):698-711.
[18]TU Y N,SENG J L.Indices of novelty for emerging topic detection[J].Information Processing&Management,2012,48(2):303-325.
[2]白如江,冷伏海,廖君华.科学研究前沿探测主要方法比较与发展趋势研究[J].情报理论与实践,2017,40(5):33-38.
[3]刘小平,冷伏海,李泽霞.国际科技前沿分析的方法和途径[J].图书情报工作,2012,56(12):60-65.
[4]PRICE D J D S.Networks of scientific papers[J].Science,1965,149(3683):510.
[5]SMALL H,GRIFFITH B C.The structure of scientific literatures I:identifying and graphing specialties[J].Science Studies,1974,4(1):17-40.
[6]KLEINBERG J.Bursty and hierarchical structure in stream[C].Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2002.
[7]PERSSON O.The intellectual base and research fronts of JASIS1986-1990[J].Journal of the Association for Information Science&Technology,1994,45(1):31-38.
[8]SMALL H.Tracking and predicting growth areas in science[J].Scientometrics,2006,68(3):595-610.
[9]杨俊林,邵久书.从美国国家科学基金会近年资助的理论与计算化学项目看该领域的发展趋势[J].中国科学基金,2005,19(5):292-294.
[10]杨荔媛,朱庆华.我国图书馆、情报与档案管理学科的研究现状——基于2000-2006年国家社会科学基金和自然科学基金立项的分析[J].情报理论与实践,2007,30(6):756-759.
[11]赵蓉英,赵浚吟,陈必坤.透视“图书馆、情报与档案管理”学科的研究主题与趋势——以2001-2012年国家科学基金为研究视角[J].情报理论与实践,2014,37(2):1-5.
[12]李英.我国图书情报与档案管理学科研究现状剖析——基于2009-2013年国家自然科学基金和国家社会科学基金立项的分析[J].图书情报工作,2014,58(9):31-36.
[13]梁伟波.美国NSF资助物流项目的知识图谱分析[J].情报杂志,2016,35(10):114-119.
[14]洪宇,张宇,刘挺,等.话题检测与跟踪的评测及研究综述[J].中文信息学报,2007,21(6):71-87.
[15]PAPKA R,ALLAN J.Topic detection and tracking:event clustering as a basis for first story detection[J].Information Retrieval,2000,7:97-126.
[16]LAM W,MENG H M L,WONG K L,et al.Using contextual analysis for news event detection[J].International Journal of Intelligent Systems,2010,16(4):525-546.
[17]范云满,马建霞.基于LDA与新兴主题特征分析的新兴主题探测研究[J].情报学报,2014,33(7):698-711.
[18]TU Y N,SENG J L.Indices of novelty for emerging topic detection[J].Information Processing&Management,2012,48(2):303-325.