情报研究

基于基金项目数据的新兴交叉主题识别——以量子技术为例

  • 邓启平 ,
  • 柯佳秀
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  • 电子科技大学图书馆 成都 611731
邓启平,馆员,硕士,E-mail:dengqp@uestc.edu.cn;柯佳秀,馆员,硕士。

收稿日期: 2023-04-07

  修回日期: 2023-07-27

  网络出版日期: 2023-11-08

基金资助

本文系电子科技大学2021年度“双一流”建设研究支持计划项目“新兴交叉学科研究主题识别方法及其在量子信息领域的应用研究”(项目编号:SYLYJ2021213)研究成果之一。

Identifying Emerging Interdisciplinary Topics Based on the Fund Project Data: A Case Study of Quantum Technology

  • Deng Qiping ,
  • Ke Jiaxiu
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  • Library, University of Electronic Science and Technology of China, Chengdu 611731

Received date: 2023-04-07

  Revised date: 2023-07-27

  Online published: 2023-11-08

摘要

[目的/意义] 为更早地揭示交叉学科领域的发展方向,研究基于基金项目数据的交叉学科领域新兴主题识别方法。[方法/过程] 将新兴主题和学科交叉主题识别方法相融合,采用定性判别和文本分析相结合的方式衡量项目的学科交叉性,并利用LDA模型从学科交叉项目的文本中探测学科交叉主题,然后基于主题支持文档和项目资助时间及金额信息,利用主题新颖度和主题强度指标测度主题的新兴程度,最后通过新兴程度的连续性遴选出新兴交叉主题。[结果/结论] 对量子技术领域进行实证分析,识别出5个新兴交叉主题,通过分析新兴交叉主题的发文趋势和学科交叉态势,在一定程度上验证了方法的有效性,研究成果对基于基金项目数据识别交叉学科领域新兴主题的方法研究与实践具有参考价值。

本文引用格式

邓启平 , 柯佳秀 . 基于基金项目数据的新兴交叉主题识别——以量子技术为例[J]. 图书情报工作, 2023 , 67(20) : 130 -141 . DOI: 10.13266/j.issn.0252-3116.2023.20.012

Abstract

[Purpose/Significance] In order to find out the development of interdisciplinary research as soon as possible, the paper explores the emerging topic identification methods of interdisciplinary fields based on fund project data. [Method/Process] This study integrated identification methods of emerging topics and interdisciplinary topics, used qualitative discrimination and textual analysis to measure the interdisciplinary characteristic of projects, and detected interdisciplinary topics from the text of projects with LDA model. Then, it measured the emerging degree of topics by novelty index and intensity index based on topic support documents and funding time and amount. Finally, it selected the emerging interdisciplinary topics according to the coherence of the emerging degree. [Results/Conclusion] This paper makes an empirical analysis of quantum technology, and identifies five emerging interdisciplinary topics. The effectiveness of this method is verified by analyzing the publication trends and interdisciplinary trends of emerging topics. The results have a certain reference value for the research and practice of identifying emerging topics in interdisciplinary fields based on fund project data.

参考文献

[1] 王效岳, 刘自强, 白如江, 等. 基于基金项目数据的研究前沿主题探测方法[J]. 图书情报工作, 2017, 61(13):87-98. (WANG X Y, LIU Z Q, BAI R J, et al. The method of research front topic detection based on the fund project data[J]. Library and information service, 2017, 61(13):87-98.)
[2] ALLAN J, CARBONELL J, DODDINGTON G, et al. Topic detection and tracking pilot study final report[C]//Proceedings of the DARPA broadcast news transcription and understanding workshop. Virginia:Lansdowne, 1998:194-218.
[3] 张婧, 刘彦君, 张炜, 等. 基于科研项目数据的科技前沿识别有效路径实证探索[J]. 科技管理研究, 2019, 39(16):108-119. (ZHANG J, LIU Y J, ZHANG W, et al. Empirical exploration on effective paths to identify frontier tech based upon data of scientific research projects[J]. Science and technology management research, 2019, 39(16):108-119.)
[4] 李荣, 刘静, 李梦辉, 等. 基于基金项目数据的人工智能技术前沿性测度研究——技术创新决策视角分析[J]. 情报杂志, 2020, 39(9):81-87. (LI R, LIU J, LI M H, et al. Frontier measurement of artificial intelligence technology:analysis based on fund project data from the perspective of technology innovation decision-making[J]. Journal of intelligence, 2020, 39(9):81-87.)
[5] 贾佳. 全球人工智能及交叉领域:基于学科布局及热点研究、潜在研究趋势的分析[J]. 科学观察, 2021, 16(5):31-52. (JIA J. Global AI and interdisciplinary fields:analysis based on discipline layout, hot research and potential research trend[J]. Science focus, 2021, 16(5):31-52.)
[6] 王文娟, 马建霞. 基于LDA的科研项目主题挖掘与演化分析——以NSF海洋酸化研究为例[J]. 情报杂志, 2017, 36(7):34-39. (WANG W J, MA J X. Topic detection and evolution analysis of research project based on LDA:a case study of projects on ocean acidification supported by NSF[J]. Journal of intelligence, 2017, 36(7):34-39.)
[7] LIMA I, RHEUBANM J. Topics and trends in NSF ocean sciences awards[J]. Oceanography 2018, 31(4):164-170.
[8] 刘自强, 岳丽欣, 朱承宁, 等. 基金项目和论文主题扩散演化路径识别及其可视化研究[J]. 现代情报, 2022, 42(11):76-86. (LIU Z Q, YUE L X, ZHU C N, et al. Research on topic diffusion evolution path identification and visualization of fund projects and papers[J]. Journal of modern information, 2022, 42(11):76- 86.)
[9] 静发冲, 李晨英, 韩明杰, 等. 基于文本挖掘的美国NSF生物科学部新兴前沿项目主题分析[J]. 现代情报, 2014, 34(12):107-112. (JING F C, LI C Y, HAN M J, et al. Topic analysis of projects from emerging frontiers division of NSF's directorate for biological science based on text mining[J]. Journal of modern information, 2014, 34(12):107-112.)
[10] 靳嘉林, 王曰芬, 巴志超, 等. 基金项目研究的主题挖掘与动态演化分析——以美国NSF数据中AI领域为例[J]. 情报学报, 2022, 41(9):967-979. (JIN J L, WANG Y F, BA Z C, et al. Topic mining and dynamic evolution analysis of funding projects:case studies of AI field in NSF data[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(9):967-979.)
[11] WANG Q. A bibliometric model for identifying emerging research topics[J]. Journal of the Association for Information Science and Technology, 2018, 69:290-304.
[12] 徐路路, 王效岳, 白如江, 等. 基于DTM模型和文本特征分析的基金项目新兴趋势探测研究——以NSF石墨烯领域为例[J]. 数据分析与知识发现, 2018, 2(3):87-97. (XU L L, WANG X Y, BAI R J, et al. Detecting emerging trends of funds based on DTM model and text analytics:case study of NSF graphene field[J]. Data analysis and knowledge discovery, 2018, 2(3):87-97.)
[13] 李静, 徐路路, 赵素君. 基于时间序列分析和SVM模型的基金项目新兴主题趋势预测与可视化研究[J]. 情报理论与实践, 2019, 42(1):118-123. (LI J, XU L L, ZHAO S J. Prediction and visualization of emerging topics of fund sponsored projects based on time series analysis and SVM model[J]. Information studies:theory & application, 2019, 42(1):118-123.)
[14] 范丽鹏, 王曰芬, 岑咏华, 等. 基金项目计划学部交叉及对前沿分布的影响研究——以美国NSF数据中AI领域为例[J]. 情报学报, 2022, 41(9):956-966. (FAN L P, WANG Y F, CEN Y H, et al. Frontier influence of the inter-directorate in project planning:case studies of AI field in NSF data[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(9):956-966.)
[15] 陈稳, 陈伟. 基于计量指标多变量LSTM模型的新兴主题热度预测研究[J]. 数据分析与知识发现, 2022, 6(10):35-45. (CHEN W, CHEN W. Predicting popularity of emerging topics with multivariable LSTM and bibliometric indicators[J]. Data analysis and knowledge discovery, 2022, 6(10):35-45.)
[16] 许海云, 董坤, 刘昊, 等. 基于异构网络的学科交叉主题发现方法[J]. 情报科学, 2017, 35(6):130-137. (XU H Y, DONG K, LIU H, et al. Interdisciplinary topics discovery method based on heterogeneous networks[J]. Information science, 2017, 35(6):130-137.)
[17] 张斌. 交叉学科主题探究:从主题聚类视角[J]. 情报科学, 2020, 38(10):49-55. (ZHANG B. Interdisciplinary subject exploration:from the perspective of topic clustering[J]. Information science, 2020, 38(10):49-55.)
[18] 阮光册, 夏磊. 学科间交叉研究主题识别——以图书情报学与教育学为例[J]. 情报科学, 2020, 38(12):152-157. (RUAN G C, XIA L. Research on interdisciplinary topics identification:a case study of library & information science and education[J]. Information science, 2020, 38(12):152-157.)
[19] 韩正琪, 刘小平, 寇晶晶. 基于Rao-Stirling指数和LDA模型 的领 域学 科交 叉主 题识 别——以纳 米科 技为 例[J]. 情报 科学, 2020, 38(2):116-124. (HAN Z Q, LIU X P, KOU J J. Interdisciplinary literature discovery based on RaoStirling diversity indices:case studies in nanoscience and nanotechnology[J]. Information science, 2020, 38(2):116-124.)
[20] 韩芳, 张生太, 冯凌子, 等. 基于专利文献技术融合测度的突破性创新主题识别——以太阳能光伏领域为例[J]. 数据分析与知识发现, 2021, 5(12):137-147. (HAN F, ZHANG S T, FENG L Z, et al. Identifying breakthrough patent topics by measuring technological convergence:case study of solar PV domain[J]. Data analysis and knowledge discovery, 2021, 5(12):137-147.)
[21] HE T, FU W, XU J, et al. Discovering interdisciplinary research based on neural networks[J]. Frontiers in bioengineering and biotechnology, 2022, 10:908733.
[22] NICHOLS L. A topic model approach to measuring interdisciplinarity at the National Science Foundation[J]. Scientometrics, 2014, 100(3):741-754.
[23] BROMHAM L, DINNAGE R, HUA X. Interdisciplinary research has consistently lower funding success[J]. Nature, 2016, 534(7609):684-687.
[24] 张雪, 张志强. 美国科学基金会资助项目的学科交叉度演化规律及影响研究[J]. 情报理论与实践, 2021, 44(12):122-132. (ZHANG X, ZHANG Z Q. Research on evolution and influence of interdisciplinarity in NSF funded projects[J]. Information studies:theory & application, 2021, 44(12):122-132.)
[25] 温芳芳, 杨倩倩, 李翔宇. 我国人文社会科学学科交叉性的测度及其演化规律研究——基于国家社科基金关键词耦合分析[J]. 现代情报, 2022, 42(3):157-167. (WEN F F, YANG Q Q, LI X Y. Research on the interdisciplinary measurement and evolution of Chinese social science and humanity:based on the keywords coupling analysis of the national social science fund projects[J]. Journal of modern information, 2022, 42(3):157-167.)
[26] 王卫军, 姚畅, 乔子越, 等. 基于词嵌入的国家自然科学基金学科交叉知识发现方法——以"人工智能"与"信息管理"为例[J]. 情报学报, 2021, 40(8):831-845. (WANG W J, YAO C, QIAO Z Y, et al. Method of discovering interdisciplinary knowledge of the national natural science foundation of China based on word embedding:a case study on artificial intelligence and information management[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(8):831-845.)
[27] 杨金庆, 张力. 学科交叉视角下新兴主题识别特征分析——以医学信息学为例[J]. 情报工程, 2021, 7(4):3-12. (YANG J Q, ZHANG L. The characteristics analysis of emerging topic identification from interdisciplinary perspective:a case study of medical informatics[J]. Technology intelligence engineering, 2021, 7(4):3-12.)
[28] 刘冬东. 新兴交叉主题识别与引文扩散影响研究——以计算生物学领域为例[D]. 郑州:华北水利水电大学, 2020. (LIU D D. The identification and citation diffusion impact of the emerging interdisciplinary topics:a case study of computational biology the identification and citation diffusion impact of the emerging[D]. Zhengzhou:North China University of Water Resources and Electric Power, 2020.)
[29] 张琳, 黄颖. 交叉科学测度、评价与应用[M]. 北京:科学出版社, 2019. (ZHANG L, HUANG Y. Interdisciplinary measurement, evaluation and application[M]. Beijing:China Science Publishing & Media, 2019.)
[30] ZHANG L, ROUSSEAU R, GLANZEL W. Diversity of references as an indicator of the interdisciplinarity of journals:taking similarity between subject fields into account[J]. Journal of the Association for Information Science and Technology, 2016, 67(5):1257-1265.
[31] BLEI D, NG A, JORDAN M. Latent Dirichlet allocation[J]. The journal of machine learning research, 2003(3):993-1022.
[32] 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. (FAN Y M, MA J X. Detection of emerging topics based on LDA and feature analysis of emerging topics[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(7):698-711.)
[33] 白敬毅, 颜端武, 陈琼. 基于主题模型和曲线拟合的新兴主题趋势预测研究[J]. 情报理论与实践, 2020, 43(7):130-136. (BAI J Y, YAN D W, CHEN Q. Trend prediction of emerging topics based on topic model and curve fitting[J]. Information studies:theory & application, 2020, 43(7):130-136.)
[34] MANN G, MIMNO D, MCCALLUM A. Bibliometric impact measures leveraging topic analysis[C]//Proceedings of the 6th ACM/IEEE-CS joint conference on digital libraries. Chapel Hill:ACM, 2006:65-74.
[35] SESKIR Z, AYDINOGLU A. The landscape of academic literature in quantum technologies[J]. International journal of quantum information, 2021, 19(2):2150012.
[36] 刘自强, 许海云, 岳丽欣, 等. 面向研究前沿预测的主题扩散演化滞后效应研究[J]. 情报学报, 2018, 37(10):979-988. (LIU Z Q, XU H Y, YUE L X, et al. Research on lagging effect of topic diffusion evolution face to prediction of research front[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(10):979-988.)
[37] LEYDESDORFFL. A method for generating overlay maps on the basis of aggregated journal-journal citation relations in 2015[EB/OL].[2023-04-02]. http://www.leydesdorff.net/wc15/wc19/index.htm.
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