图书情报工作

图书情报工作 >

2024 , Vol. 68 >Issue 10: 97 - 108

DOI: https://doi.org/10.13266/j.issn.0252-3116.2024.10.009

情报研究

“问题—方法”关联视角下领域知识创新网络演化机制研究——以信息资源管理学科群为例

  • 杨金庆 ,
  • 庞业佳 ,
  • 刘智锋 ,
  • 李鹏程 ,
  • 李洁
展开
  • 1 华中师范大学信息管理学院 武汉 430079;
    2 北京大学信息管理系 北京 100871;
    3 湖北工业大学经济与管理学院 武汉 430068;
    4 苏州大学社会学院 苏州 215000
杨金庆，特任副教授，博士，E-mail: jinq_yang@163.com；庞业佳，本科生；刘智锋，博士研究生；李鹏程，讲师，博士；李洁，讲师，博士。

收稿日期: 2023-09-26

  修回日期: 2024-01-16

  网络出版日期: 2024-06-04

基金资助

本文系国家重点研发子课题“颠覆性技术识别理论、方法与专家预判系统”（项目编号：2019YFA0707200）、国家自然科学基金项目“基于可解释机器学习的科学知识角色转变预测研究”（项目编号：72304108）、江苏省社会科学青年基金项目“疫情常态化背景下图书馆数字资源认知推荐研究”（项目编号：21TQC001）研究成果之一。

收起

Research on the Evolution Mechanism of Domain Knowledge Innovation Network from the Perspective of “Issue-Method” Correlation: Taking the Discipline Group of Information Resources Management as an Example

  • Yang Jinqing ,
  • Pang Yejia ,
  • Liu Zhifeng ,
  • Li Pengcheng ,
  • Li Jie
Expand
  • 1 School of Information Management, Central China Normal University, Wuhan 430079;
    2 Department of Information Management, Peking University, Beijing 100871;
    3 College of Economics and Management, Hubei University of Technology, Wuhan 430068;
    4 College of Social Sciences, Soochow University, Suzhou 215000

Received date: 2023-09-26

  Revised date: 2024-01-16

  Online published: 2024-06-04

Supported by

This work is supported by the National Key Research and Development Program of China titled “Theory, Method and Expert Prediction System for Disruptive Technology Identification” (Grant No. 2019YFA0707200), and the National Natural Science Foundation of China titled “Research on Role Change Prediction of Scientific Knowledge Based on Explainable Machine Learning” (Grant No. 72304108), and the Jiangsu Provincial Social Science Foundation for Youths titled “Research on Library Digital Resource Cognition Recommendation under the Normalization of epidemic situation” (Grant No. 21TQC001).

Fold

摘要

[目的/意义]从“问题—方法”关联视角，探究组合创新模式下的领域知识创新网络的演化机制，试图揭示科学知识创新的潜在影响因素，以便辅助科技政策制定，及时开辟创新发展新赛道。[方法/过程]首先选取信息资源管理学科群，识别领域“问题”与“方法”知识单元，然后以“问题—方法”关联为组合单元，构建领域知识创新网络，最后运用以关系为中心的统计建模方法—指数随机图模型对关系形成及关系模式进行建模，分析网络节点属性特征、网络结构特征等多种内生性和外生性因素对领域知识创新网络形成与演化的影响。[结果/结论]研究发现，期刊分散广度较大的“问题”与“方法”类知识单元形成连边的可能性相对较大，学科领域内部的“问题”与“方法”间形成连边的倾向相对较大，刊载在影响力相对较低的期刊上的“问题”与“方法”相对较易形成连边，下载量较高或被引频次相对较低的“问题”与“方法”知识单元之间形成连边可能性较大。

关键词： 知识网络; 演化机制; 科技创新; 组合创新

本文引用格式

杨金庆 , 庞业佳 , 刘智锋 , 李鹏程 , 李洁 . “问题—方法”关联视角下领域知识创新网络演化机制研究——以信息资源管理学科群为例[J]. 图书情报工作, 2024 , 68(10) : 97 -108 . DOI: 10.13266/j.issn.0252-3116.2024.10.009

Abstract

[Purpose/Significance] From the correlation of “issue-method”, this study explores the evolution mechanism of domain knowledge innovation networks under the combined innovation mode, reveals the potential influencing factors of scientific knowledge innovation, in order to assist in science and technology policy formulation and timely open up new way of innovation and development. [Method/Process] This paper focused on the discipline group of information resource management, identified the knowledge units of “issues” and “methods” in it, and then constructed a domain knowledge innovation network using “Issues-Method” association as a combination unit. Finally, it constructed the model on the relationship formation and relationship patterns with the relationship centered statistical modeling method, Exponential Random Graph Model, and analyzed the impact of various endogenous and exogenous factors, such as network node attribute and network structure, on the formation and evolution of domain knowledge innovation networks. [Result/Conclusion] It shows that the possibility of “issue-method” correlation is relatively large in journals with large dispersion breadth. Within disciplines, the tendency of “issue-method” correlation is relatively high. “Issues-method” correlation in journals with relatively low impact are more easily to form edges. In literatures with high downloads or relatively low citation frequency, it is more likely that the edge between “issues” and “method” knowledge units is formed.

Key words： knowledge network; evolution mechanism; technological innovation; combinatorial innovation

参考文献

[1] UZZI B, MUKHERJEE S, STRINGER M, et al. Atypical combinations and scientific impact[J]. Science, 2013, 342(6157): 468-472.
[2] 钱佳佳, 罗卓然, 陆伟. 基于问题-方法组合的科技论文新颖性度量与创新类型识别[J]. 图书情报工作, 2021, 65(14): 82-89. (QIAN J J, LUO Z R, LU W. Novelty measurement and innovation type identification of scientific literature based on question-method combination[J]. Library and information service, 2021, 65(14): 82-89.)
[3] 程齐凯, 李信, 陆伟. 领域无关学术文献词汇功能标准化数据集构建及分析[J]. 情报科学, 2019, 37(7): 41-47. (CHENG Q K, LI X, LU W. Construction and analysis of standard data set for domain-independent term function in academic texts[J]. Information science, 2019, 37(7): 41-47.)
[4] HEFFERNAN K, TEUFEL S. Identifying problems and solutions in scientific text[J]. Scientometrics, 2018, 116(2): 1367-1382.
[5] 陆伟, 李鹏程, 张国标, 等. 学术文本词汇功能识别——基于BERT向量化表示的关键词自动分类研究[J]. 情报学报, 2020, 39(12): 1320-1329. (LU W, LI P C, ZHANG G B, et al. Recognition of lexical functions in academic texts: automatic classification of keywords based on BERT vectorization[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(12): 1320-1329.)
[6] 张国标, 李鹏程, 陆伟, 等. 多特征融合的关键词语义功能识别研究[J]. 图书情报工作, 2021, 65(9): 89-96. (ZHANG G B, LI P C, LU W, et al. Research on keyword semantic function recognition based on multi-feature fusion[J]. Library and information service, 2021, 65(9): 89-96.)
[7] 程齐凯, 李鹏程, 张国标, 等. 学术文本词汇功能识别——基于标题生成策略和注意力机制的问题方法抽取[J]. 情报学报, 2021, 40(1): 43-52. (CHENG Q K, LI P C, ZHANG G B, et al. Recognition of lexical functions in academic texts: problem method extraction based on title generation strategy and attention mechanism [J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(1): 43-52.)
[8] 张颖怡, 章成志, 周毅, 等. 基于ChatGPT的多视角学术论文实体识别：性能测评与可用性研究[J]. 数据分析与知识发现, 2023, 7(9): 12-24. (ZHANG Y Y, ZHANG C Z, ZHOU Y, et al. ChatGPT-Based scientific paper entity recognition: performance measurement and availability research[J]. Data analysis and knowledge discovery, 2023, 7(9): 12-24.)
[9] 丁玉飞, 关鹏. 知识进化视角下科学文献传播网络演化与预测研究及应用[J]. 图书情报工作, 2018, 62(4): 72-80. (DING Y F, GUAN P. Study and application of translation and prediction of the scientific literature communication network from the perspective of knowledge evolution[J]. Library and information service, 2018, 62(4): 72-80.)
[10] 操玉杰, 毛进, 潘荣清, 等. 学科交叉研究的演化阶段特征分析——以医学信息学为例[J]. 数据分析与知识发现, 2019, 3(5): 107-116. (CAO Y J, MAO J, PAN R Q, et al. Analyzing characteristics of interdisciplinary research evolutions: case study of medical informatics[J]. Data analysis and knowledge discovery, 2019, 3(5): 107-116.)
[11] BEHROUZI S, SARMOOR Z S, HAJSADEGHI K, et al. Predicting scientific research trends based on link prediction in keyword networks[J]. Journal of informetrics, 2020, 14(4): 101079.
[12] MINA A, RAMLOGAN R, TAMPUBOLON G, et al. Mapping evolutionary trajectories: applications to the growth and transformation of medical knowledge[J]. Research policy, 2007, 36(5): 789-806.
[13] LIANG Z, LIU F, MAO J, et al. A knowledge representation model for studying knowledge creation, usage, and evolution[C]//International conference on information. Berlin: Springer, Cham, 2021: 97-111.
[14] ZHANG X, XIE Q, SONG C, et al. Mining the evolutionary process of knowledge through multiple relationships between keywords[J]. Scientometrics, 2022, 127(4): 2023-2053.
[15] 杨冠灿, 陈亮, 张静, 等. 专利引用关系形成的解释框架:一个指数随机图模型视角[J]. 图书情报工作, 2019, 63(5): 100-109. (YANG G C, CHENG L, ZHANG J, et al. Framework for explanations of patent citation formation: an exponential random graph model perspective[J]. Library and information service, 2019, 63(5): 100-109.)
[16] 段庆锋, 马丹丹. 基于指数随机图模型的专利技术扩散机制实证研究[J]. 科技进步与对策, 2018, 35(22): 23-29. (DUAN Q F, MA D D. Empirical research of patent technology diffusion mechanism based on ERGM[J]. Science & technology progress and policy, 2018, 35(22): 23-29.)
[17] 操玉杰, 李纲, 毛进, 等. 基于ERGM的学科交叉领域知识连接机制实证研究[J]. 图书情报工作, 2019, 63(19): 128-135. (CAO Y J, LI G, MAO J, et al. An empirical study on knowledge connection mechanism of interdisciplinary field based on ERGM[J]. Library and information service, 2019, 63(19): 128-135.)
[18] 宫雪, 崔雷. 基于医学主题词共现网络的链接预测研究[J]. 情报杂志, 2018, 37(1): 66-71, 52. (GONG X, CUI L. Link prediction in MeSH terms co-occurring networks[J]. Journal of information, 2018, 37(1): 66-71, 52.)
[19] 彭陶, 王建冬, 孙慧明. 基于关键词共现网络的我国图情领域近三十年学科发展脉络分析[J]. 大学图书馆学报, 2012, 30(2): 29-34. (PENG T, WANG J D, SUN H M. An analysis on development vein of Chinese library and information science during the past 30 years based on keywords co-occurrence network[J]. Journal of academic libraries, 2012, 30(2): 29-34.)
[20] 王晓光. 科学知识网络的形成与演化(II): 共词网络可视化与增长动力学[J]. 情报学报, 2010(2): 314-322. (WANG X G. Formation and evolution of science knowledge network(Ⅱ): co-word network visualization and growth dynamics[J]. Journal of the China Society for Scientific and Technical Information, 2010(2): 314-322.)
[21] 程齐凯. 学术文本的词汇功能识别[D]. 武汉: 武汉大学, 2015. (CHENG Q K. Recognition of lexical functions in academic texts [D]. Wuhan: Wuhan University, 2015.)
[22] 陆伟, 李鹏程, 张国标, 等. 学术文本词汇功能识别——基于BERT向量化表示的关键词自动分类研究[J]. 情报学报, 2020, 39(12): 1320-1329. (LU W, LI P C, ZHANG G B, et al. Recognition of lexical functions in academic texts: automatic classification of keywords based on BERT vectorization[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(12): 1320-1329.)
[23] ECK N J, WALTMAN L. How to normalize co-occurrence data? An analysis of some well‐known similarity measures[J]. Journal of the American Society for Information Science and Technology, 2009, 60(8): 1635-1651.
[24] HUNTER D R, HANDCOCK M S, BUTTS C T, et al. Ergm: a package to fit, simulate and diagnose exponential-family models for networks[J]. Journal of statistical software, 2008, 24(3): 1-29.
[25] 杨冠灿, 陈亮, 张静, 等. 专利引用关系形成的解释框架:一个指数随机图模型视角[J]. 图书情报工作, 2019, 63(5): 100-109. (YANG G C, CHENG L, ZHANG J, et al. Framework for explanations of patent citation formation: an exponential random graph model perspective[J]. Library and information service, 2019, 63(5): 100-109.)
[26] MOED H F. Measuring contextual citation impact of scientific journals[J]. Journal of informetrics, 2010, 4(3): 265-277.
[27] DINESH K S. Ranking of arts and humanities journals published in India: a scientometric analysis[J]. Pearl: a journal of library and information science, 2017, 11(2): 155-158.
[28] HUANG Y, BU Y, DING Y, et al. Partitioning highly, medium and lowly cited publications[J]. Journal of information science, 2021, 47(5): 609-614.
[29] KRIVITSKY P N, HUNTER D R, MORRIS M, et al. Ergm 4: new features for analyzing exponential-family random graph models[J]. Journal of statistical software, 2023, 105(6): 1-44.
[30] PESET F, GARZÓN-FARINÓS F, GONZÁLEZ L M, et al. Survival analysis of author keywords: an application to the library and information sciences area[J]. Journal of the Association for Information Science and Technology, 2020, 71(4): 462-473.
Options
文章导航

/

〈 〉