图书情报工作 ›› 2022, Vol. 66 ›› Issue (23): 21-28.DOI: 10.13266/j.issn.0252-3116.2022.23.003

所属专题: 知识输出视角下情报学学科影响力研究

• 专题: 知识输出视角下情报学学科影响力研究 • 上一篇    下一篇

知识归属视角下情报学对自然科学的知识输出及作用分析

荣国阳, 李长玲, 栾锟, 徐卫杰   

  1. 山东理工大学信息管理研究院 淄博 255049
  • 收稿日期:2022-05-31 修回日期:2022-09-16 发布日期:2022-12-16
  • 通讯作者: 李长玲,教授,硕士,硕士生导师,通信作者,E-mail:lichl69@163.com
  • 作者简介:荣国阳,硕士研究生;栾锟,硕士研究生;徐卫杰,硕士研究生。
  • 基金资助:
    本文系国家社会科学基金重点项目"跨学科潜在知识生长点识别与创新趋势预测研究"(项目编号:19ATQ006)研究成果之一。

Analysis on the Knowledge Output and Its Function of Information Science to Natural Science: from the Perspective of Knowledge Attribution

Rong Guoyang, Li Changling, Luan Kun, Xu Weijie   

  1. Institute of Information Management, Shandong University of Technology, Zibo 255049
  • Received:2022-05-31 Revised:2022-09-16 Published:2022-12-16

摘要: [目的/意义] 识别情报学对自然科学的知识输出并分析其贡献,有利于把握情报学在学术体系中的角色定位,对探索学科地位提升策略具有重要意义。[方法/过程] 采用跨学科推动力模型识别输出知识γ及其作用知识β,即知识输出组合γ-β,并构建知识归属度模型判别知识γ的主研究学科,进而识别核心知识输出、中介知识输出和知识反哺。[结果/结论] 发现情报学对自然科学学术体系的贡献主要有两类,一是输出文献计量等核心研究方法,帮助完善知识图谱领域的研究体系,二是通过对文本分析方法的实践与应用,反哺机器学习等算法的创新。

关键词: 知识归属, 跨学科推动力, 情报学影响力, 自然科学, 知识输出

Abstract: [Purpose/Significance] Identifying the knowledge output of information science to the natural sciences and analyzing its contribution is of great significance for grasping the role of information science in the academic system, and to explore strategies for improving the status of it. [Method/Process] Use the interdisciplinary impetus model to identify the output knowledge γ and its affected knowledge β, which is knowledge output combination γ-β. Then, construct a knowledge attribution model to identify the main research discipline of knowledge γ, to identify the core knowledge output, intermediary knowledge output and knowledge feedback. [Result/Conclusion] We find that, there are two main contributions of information science to the academic system of natural science. One is to export core research methods such as bibliometrics to help improve the research system in the field of knowledge graph. The other is to practice and apply text analysis methods, to feed back the innovation of algorithms such as machine learning.

Key words: knowledge attribution, interdisciplinary impetus, informatics influence, natural science, knowledge output

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