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
Publication Date
4-1-2024
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.