信息资源管理学报 ›› 2025, Vol. 15 ›› Issue (5): 147-161.doi: 10.13365/j.jirm.2025.05.147

• 研究论文 • 上一篇    下一篇

NSFC资助视角下的信息资源管理学科发展研究

胡吉明1,2,3 杨云1   

  1. 1.武汉大学信息管理学院,武汉,430072; 
    2.武汉大学大数据研究院,武汉,430072; 
    3.武汉大学数据智能研究院,武汉,430072
  • 出版日期:2025-09-26 发布日期:2025-10-31
  • 作者简介:胡吉明,博士,教授,博士生导师,研究方向为自然语言推理与政策智能;杨云(通讯作者),硕士研究生,研究方向为档案与政务信息学,Email:yunyang@whu.edu.cn。
  • 基金资助:
    本文系国家社会科学基金一般项目“基于结构功能的政策文本摘要生成研究”(23BTQ081)的研究成果。

Research on the Development of Information Resources Management Discipline from the Perspective of NSFC Funding

Hu Jiming1,2,3 Yang Yun1   

  1. 1.School of Information Management, Wuhan University, Wuhan, 430072; 
    2.Big Data Research Institute, Wuhan University, Wuhan, 430072; 
    3.Institute of Data Intelligence, Wuhan University, Wuhan, 430072
  • Online:2025-09-26 Published:2025-10-31
  • About author:Hu Jiming, Ph.D., professor, doctoral supervisor with research interests in natural language reasoning and policy intelligence; Yang Yun(corresponding author), master candidate, with research interests in archives and government informatics, Email: yunyang @whu.edu.cn.
  • Supported by:
    This research is supported by the National Social Science Foundation of China, "Research on Policy Text Summary Generation Based on Structural Function"(23BTQ081).

摘要: 从国家自然科学基金项目的角度,通过内容深度挖掘总结与提炼信息资源管理学科研究的主题和方向,把握转型中的信息资源管理学科发展趋势。构建融合主题挖掘和深度学习模型的学科发展态势分析框架,基于NSFC近五年G0414学科的项目数据,开展项目主题识别、申请与资助主题差异、主题关联结构和演化的可视化分析。研究发现,信息资源管理学科的资助率较为稳定且处于较高水平,集中于学科排名前列的少数高校;少数主题方向获得了最终的资助,主要包括信息需求、多模态计算、知识发现、数智赋能、大模型、风险管理等,主题之间存在不同程度关联和影响,且具有年代的持续演化特征;信息资源管理学科整体发展态势良好,资助总量稳定且研究方向有所侧重,但申请与资助的认知存在较大差异。

关键词: 信息资源管理, 学科发展态势, 自科基金项目, 主题挖掘, 内容表示模型

Abstract: From the perspective of National Natural Science Foundation projects, this study delves into the theme and direction synthesis of research in the field of Information Resources Management, aiming to grasp the trends in this discipline as it undergoes transformation. A framework for analysing the developmental trends of the discipline has been constructed, integrating theme mining and deep learning models. Based on the NSFC's G0414 project data from the past five years, this study conducts visual analyses of project theme identification, differences between application and funding themes, thematic association structures, and their evolution over time.The funding ratio for the Information Resource Management discipline remains stable and at a high level, primarily concentrated among a few top-ranking universities. A limited number of thematic areas have received final funding, including information demand, multimodal computing, knowledge discovery, intelligent empowerment, large models, and risk management. These themes exhibit varying degrees of interconnection and influence, characterized by ongoing evolutionary traits. In the realm of natural science research, the overall development of the Information Resource Management discipline is promising, with stable funding levels focusing on specific research directions. However, there is a significant discrepancy between project applications and funding outcomes.

Key words: Information resources management, Developmental trends of the discipline, Natural science foundation projects, Theme mining, Content representation models

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