图书情报工作 ›› 2020, Vol. 64 ›› Issue (21): 111-119.DOI: 10.13266/j.issn.0252-3116.2020.21.014

• 知识组织 • 上一篇    下一篇

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

牌艳欣, 李长玲, 徐璐   

  1. 山东理工大学科技信息研究所 淄博 255049
  • 收稿日期:2020-06-03 修回日期:2020-07-21 出版日期:2020-11-05 发布日期:2020-11-05
  • 通讯作者: 李长玲(ORCID:0000-0001-6266-4820),教授,硕士生导师,通讯作者,E-mail:lichl69@163.com
  • 作者简介:牌艳欣(ORCID:0000-0001-6266-4820),硕士研究生;徐璐(ORCID:0000-0002-6086-464X),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。

Discussion on the Method of Interdisciplinary Related Knowledge Combination Identification on the Perspective of Weak Citation Relationship——Taking Information Science for Example

Pai Yanxin, Li Changling, Xu Lu   

  1. Science and Technology Information Research Institute, Shandong University of Technology, Zibo 255049
  • Received:2020-06-03 Revised:2020-07-21 Online:2020-11-05 Published:2020-11-05
  • Supported by:
     

摘要: [目的/意义] 科学系统的复杂化,使跨学科合作成为现代科学创新研究的重要范式和必然趋势。识别具有高度合作潜力的跨学科相关知识组合,成为促进跨学科科研合作创新的关键。[方法/过程] 首先,选择目标学科源文献及其跨学科参考文献、跨学科引证文献,构建基于关键词的跨学科知识弱引文关联网络;其次,划分知识媒介b的类型,并识别目标学科知识节点a-知识媒介b-跨学科相关知识c的弱关系结构;最后,定义目标学科知识节点影响力指数AI、知识媒介影响力指数BI、跨学科知识相关性指数CI、跨学科知识组合a-c潜在合作指数P,识别合作潜力值高的跨学科相关知识组合。[结果/结论] 选择情报学领域9种CSSCI期刊2015-2019年的载文及其跨学科参考与引证文献为样本进行实证研究,验证基于弱引文关系的跨学科相关知识组合发现方法的有效性与可行性,并识别得到"科研合作"-"知识流动"-"种群动力学模型"等情报学科的高合作潜力的跨学科相关知识组合。

 

关键词: 弱关系, 引文分析, a-b-c弱连接, 跨学科相关知识组合, 潜在合作指数P

Abstract: [Purpose/significance] With the complexity of the scientific system, interdisciplinary research has become an important paradigm and inevitable trend of modern scientific innovation research. Identifying interdisciplinary relevant knowledge combination that has high cooperation potential, becomes the key to promoting interdisciplinary scientific research cooperation and innovation. [Method/process] Firstly,the paper selected target subject source literature,its interdisciplinary reference literature, and its interdisciplinary citing literature, so as to construct an interdisciplinary knowledge weak reference relational network based on keywords. Secondly, it classified the types of Knowledge Medium b, and identified the weak relational structure of the Knowledge Node a of the target discipline-Knowledge Medium b - Interdisciplinary Knowledge c. Finally, the paper defined the Knowledge Node Influence Index AI of the target discipline, the Knowledge Media Influence Index BI, the Interdisciplinary Knowledge Correlation Index CI, the Interdisciplinary Knowledge Combination a-c and Potential Cooperation Index P, to identify the interdisciplinary knowledge combination with high cooperation potential. [Result/conclusion] The papers of 9 CSSCI journals in the field of informatics from 2015 to 2019 and their interdisciplinary references and citations were selected as samples for empirical research, to verify the effectiveness and feasibility of the discovery method of interdisciplinary knowledge combination based on weak citation relationship, and to identify the interdisciplinary knowledge combination with high cooperation potential of intelligence disciplines such as "scientific research cooperation"-"knowledge flow"-"population dynamics model".

Key words: strength of weak ties, citation analysis, a-b-c weak connection, interdisciplinary related knowledge combination, potential cooperation index P

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