Scientific Information Research
Keywords
interdisciplinary knowledge; National Natural Science Foundation; knowledge growth path; interdisciplinary measurement indicators
Abstract
[Purpose/significance]In the application for NSFC projects, the same scholar uses different fund codes at different times, which promotes the integration and growth of interdisciplinary knowledge to a certain extent. Therefore, based on the interdisciplinary application for the National Natural Science Foundation of China, this paper explores interdisciplinary knowledge and its integrated growth path. [Method/process]The interdisciplinary measurement indicators were improved and optimized based on the hierarchical structure of NSFC discipline application code to identify the most interdisciplinary knowledge. Subsequently, a type of heterogeneous network of interdisciplinary knowledge and first-level disciplines was constructed, and interdisciplinary knowledge community discovery and growth path mining were realized based on RankClus. [Result/conclusion]Through research, it is found that there are 12 significant interdisciplinary knowledge clusters and 6 obvious interdisciplinary knowledge growth paths. Their interdisciplinary knowledge paths are Life Science-Medical Science （C-H）, Chemical Science-Engineering and Materials Science （B-E）, Life Science-Earth Science（C-D）, Mathematical and Physical Science-Information Science-Management Science（A-F-G）, Mathematical and Physical Science-Earth Science-Engineering and Materials Science （A-D-E），Chemical Science-Management Science（B-G）.
First Page
58
Last Page
71
Submission Date
December 2023
Revision Date
February 2024
Acceptance Date
February 2024
Publication Date
April 2024
Recommended Citation
WU, Xiaolan and Chengzhi, ZHANG
(2024)
"Interdisciplinary Knowledge Discovery and Knowledge Growth Path Mining: Perspective of Interdisciplinary Application of National Natural Science Foundation of China,"
Scientific Information Research: Vol. 6:
Iss.
2, Article 6.
Available at:
https://eng.kjqbyj.com/journal/vol6/iss2/6
Reference
[1] 中华人民共和国教育部. 关于高等学校加快 "双一流" 建设的指导意见 [EB/OL]. (2018-08-27) [2023-11-05].
http://www.moe.gov.cn/srcsite/A22/moe_843/201808/t20180823_345987.html.
[2] 曹树金, 曹茹烨. 基于知识图谱支持科研创新的跨学科知识发现研究 [J]. 情报理论与实践, 2022, 45(11): 10-20.
[3] WU J, JIN M, DING X H. Diversity of individual research disciplines in scientific funding [J]. Scientometrics: An International Journal for All Quantitative Aspects of the Science of Science Policy, 2015, 103(02): 669-686.
[4] 吴江, 金妙. 基于基金代码共现的学科知识流动网络研究 [J]. 情报杂志, 2016, 35(06): 23-28.
[5] 樊红侠. 知识发现及其在数字图书馆的应用 [J]. 现代情报, 2008(08): 90-92.
[6] 章成志, 吴小兰. 跨学科研究综述 [J]. 情报学报, 2017, 36(05): 523-535.
[7] 路甬祥. 学科交叉与交叉科学的意义 [J]. 中国科学院院刊, 2005, 20(01): 58-60.
[8] 李佳蕾, 安培浚, 肖仙桃. 学科交叉主题识别方法研究综述 [J]. 数据分析与知识发现, 2023, 7(04): 1-15.
[9] CHI R, YOUNG J. The interdisciplinary structure of research on intercultural relations: A co-citation network analysis study [J]. Scientometrics, 2013(96): 147-171.
[10] WANG Q. Measuring Interdisciplinarity of a Given Body of Research[C]//The 10th International Conference of the International Society for Scientometrics and Informetrics. Leuven, Leuven University Press, 2015: 372-383.
[11] 闵超, 孙建军. 基于关键词交集的学科交叉研究热点分析: 以图书情报学和新闻传播学为例 [J]. 情报杂志, 2014, 33(05): 76-82.
[12] XU H, GUO T, YUE Z, et al. Interdisciplinary topics of information science: a study based on the terms interdisciplinarity index series [J]. Scientometrics: An International Journal for All Quantitative Aspects of the Science of Science Policy, 2016, 106(02): 583-601.
[13] ABRAMO G, D'ANGELO C A, COSTA D F. Identifying interdisciplinarity through the disciplinary classification of coauthors of scientific publications [J]. Journal of the American Society for Information Science and Technology, 2012, 63(11): 2206-2222.
[14] HE B, DDING Y, TANG J, et al. Mining diversity subgraph in multidisciplinary scientific collaboration networks: A meso perspective [J]. Journal of Informetrics, 2013, 7(01): 117-128.
[15] 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(02): 116-124.
[16] 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.
[17] SMALL. Maps of science as interdisciplinary discourse: co-citation contexts and the role of analogy [J]. Scientometrics: An International Journal for All Quantitative Aspects of the Science of Science Policy, 2010, 83(03): 835-849.
[18] 章成志, 徐庶睿, 卢超. 利用引文内容监测多学科交叉现象的方法与实证 [J]. 图书情报工作, 2016, 60(19): 108-115.
[19] 杜德慧, 李长玲, 相富钟, 等. 基于引文关键词的跨学科相关知识发现方法探讨 [J]. 情报杂志, 2020, 39(09): 189-194.
[20] 徐璐, 李长玲, 王浩, 等. 基于当采中间人的跨学科相关知识组合识别: 以图书情报领域为例 [J]. 情报理论与实践, 2023, 46(10): 115-120, 106.
[21] 周娜, 李秀霞, 高丹. 基于LDA主题模型的 "作者—内容—方法" 多重共现分析: 以图书情报学为例 [J]. 情报理论与实践, 2019, 42(06): 144-148, 123.
[22] 张振刚, 罗泰晔. 基于知识组合理论的技术机会发现 [J]. 科研管理, 2020, 41(08): 220-228.
[23] 牌艳欣, 李长玲, 徐璐. 弱引文关系视角下跨学科相关知识组合识别方法探讨: 以情报学为例 [J]. 图书情报工作, 2020, 64(21): 111-119.
[24] 李长玲, 高峰, 牌艳欣. 试论跨学科潜在知识生长点及其识别方法 [J]. 科学学研究, 2021, 39(06): 1007-1014.
[25] 荣国阳, 李长玲, 范晴晴, 等. 基于生命周期理论的跨学科知识生长点识别: 以引文分析领域为例 [J]. 情报理论与实践, 2022, 45(06): 9-16.
[26] 李长玲, 范晴晴, 荣国阳, 等. 动能理论视角下跨学科知识生长点成长态势分析: 以图书情报领域为例 [J]. 情报理论与实践, 2023, 46(03): 9-15.
[27] SWANSON D R. Fish Oil, Raynaud's Syndrome, and Undiscovered Public Knowledge [J]. Perspectives in Biology & Medicine, 1986, 30(01): 7-18.
[28] GIANNETTI F. ‘So near while apart’: Correspondence Editions as Critical Library Pedagogy and Digital Humanities Methodology [J]. The Journal of Academic Librarianship, 2019, 45(05): 102033.
[29] 黄水清, 程冲, 李志燕. 开放式非相关文献知识发现方法在中文文献中的验证 [J]. 情报理论与实践, 2008(02): 246-250.
[30] 李勇, 冷伏海, 王林. 基于非相关文献的三阶知识发现方法探讨 [J]. 中国图书馆学报, 2011, 37(04): 21-26, 69.
[31] 王忠义, 彭思源, 夏立新. 跨学科知识组织的概念关联研究 [J]. 中国图书馆学报, 2022, 48(03): 43-62.
[32] 吴小兰, 章成志. 国家自然科学基金视角下学科跨学科性演变研究 [J]. 科技情报研究, 2022, 4(03): 20-32.
[33] SUN Y, HAN J, ZHAO P, et al. Rankclus: integrating clustering with ranking for heterogeneous information network analysis [C] //Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, 2009: 565-576.