大数据 ›› 2019, Vol. 5 ›› Issue (5): 3-15.doi: 10.11959/j.issn.2096-0271.2019037

• 专题:学术大数据 • 上一篇    下一篇

学术大数据技术在科技管理过程中的应用

梁英1,张伟1,2,余知栋1,2,史红周1   

  1. 1 中国科学院计算技术研究所,北京 100190
    2 中国科学院大学,北京 100190
  • 出版日期:2019-09-15 发布日期:2019-10-11
  • 作者简介:梁英(1962-),女,中国科学院计算技术研究所高级工程师,主要研究方向为大数据分析挖掘、网络内容安全和隐私保护。|张伟(1993-),男,中国科学院计算技术研究所硕士生,主要研究方向为网络表示学习、学术大数据。|余知栋(1996-),男,中国科学院计算技术研究所硕士生,主要研究方向为物端协同计算、大数据技术。|史红周(1971-),男,中国科学院计算技术研究所高级工程师,主要研究方向为物端协同计算、物联网安全、大数据技术。
  • 基金资助:
    国家重点研发计划基金资助项目(2018YFB1004700)

Applications of academic big data in the process of science and technology management

Ying LIANG1,Wei ZHANG1,2,Zhidong YU1,2,Hongzhou SHI1   

  1. 1 Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
    2 University of Chinese Academy of Sciences,Beijing 100190,China
  • Online:2019-09-15 Published:2019-10-11
  • Supported by:
    The National Key Research and Development Program of China(2018YFB1004700)

摘要:

学术大数据逐步成为提升科技管理水平的重要数据基础。通过调研国内外科技管理信息化的发展现状和特点,总结了学术大数据的发展及应用,分析了学术大数据在科技管理过程应用中面临的问题。结合我国科技管理的应用需求,设计了基于学术大数据的科技管理应用框架。基于知识图谱的学者画像构建技术和基于网络表示学习的相似作者推荐技术,利用多源异构的学术大数据,进行科研布局和资源统筹辅助决策以及科技管理过程中的专家精准推荐和成果评估评价,为提高科技管理效率提供了有效的技术支撑。

关键词: 学术大数据, 科技管理, 知识图谱, 网络表示学习, 专家推荐

Abstract:

Academic big data is gradually recognized as an important data foundation for improving the level of science and technology management.Status quo and characteristics of science and technology management informationization were investigated at home and abroad,the development and applications of academic big data were summarized,and the problems were analyzed in applications of academic big data in the process of science and technology management.The needs of science and technology management application were combined,a technology management application framework based on academic big data was designed,scholarly image construction based on knowledge graph and similar author recommendation technology based on network representation learning were applied to assist in improving the overall layout of scientific research and resources utilizing multi-heterogeneous academic big data collection.Effective technical support for improving the efficiency of science and technology management was provided.

Key words: academic big data, science and technology management, knowledge graph, network representation learning, experts recommendation

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