图书馆杂志

图书馆杂志 ›› 2020, Vol. 39 ›› Issue (9): 56-63.

• 理论探索 • 上一篇    下一篇

面向研究前沿识别的载体-特征-关系融合模型研究

冯 佳 穆晓敏 王 伟   

  • 出版日期:2020-09-17 发布日期:2020-09-17
  • 作者简介:冯  佳 女,吉林大学公共卫生学院医学信息学系, 讲师。研究方向:文本挖掘的知识发现的方法与技 术。作者贡献:参与研究方向、理论基础及研究方法 的确立,参与论文撰写。E-mail:fengjia@jlu.edu. cn 吉林长春 130021 穆晓敏 女,吉林大学公共卫生学院医学信息学系, 博士研究生。研究方向:文本挖掘理论与方法。作者 贡献:参与论文撰写。 吉林长春 130021 王  伟 吉林大学公共卫生学院医学信息学系,教 授,博士生导师。研究方向:科学计量分析、文本挖 掘与知识发现。作者贡献:参与论文撰写与修订。 吉林长春 130021

Carrier-Feature-Relationship Fusion Model for Research Fronts Identification

Feng Jia, Mu Xiaomin, Wang Wei   

  • Online:2020-09-17 Published:2020-09-17

摘要: 及时准确地把握研究前沿有助于为科技政策的制定和科研部署提供更加全面的决策依据和参考。大数据环境下,有效协同利用多源数据识别研究前沿成为当下情报学领域的研究重点。文章通过文献调研和内容分析法对研究前沿的识别方法和多源数据融合方法进行深入分析。依据研究对象不同,本文将研究前沿识别方法划分为基于引文的、基于词汇的、基于主题的和基于融合的方法,并对比阐述了基于融合方法的必要性。在多源数据融合方法方面,依据融合深度不同,本文从载体融合和关系融合两个方面梳理现有方法的特点和不足。为实现基于多源数据和深入语义层面的研究前沿识别,本文构建了面向多源科技文本融合的载体-特征-关系融合模型,并以研究前沿的核心特征为切入点,提出关注度、新颖度和中心度3个识别指标,丰富了基于多源数据融合的研究前沿识别方法。

关键词: 多源数据融合 载体融合 特征融合 关系融合 研究前沿识别 主题模型

Abstract: Timely and accurate grasp of research fronts can provide a more comprehensive decisionmaking basis and reference for the formulation of scientific and technological policies and scientific research deployment. In the era of big data, the effective and cooperative use of multi-source data to identify research fronts has become the research focus in the field of information science. Based on the literature review and content analysis, this paper analyzed the method of the research fronts identification and multi-source data fusion in depth. According to the different research objects, this paper divided the identification methods into citation-based, lexical-based, topic-based and fusion-based methods, compared the difference, and expounded the necessity of fusion-based methods. In terms of multi-source data fusion methods, this paper reviewed the characteristics and shortcomings of existing methods from carrier fusion and relationship fusion, according to the depth of fusion. We constructed a carrier-feature-relationship fusion model for multi-source scientific text fusion. What’s more, based on analyzing the core features of research fronts, we proposed three identifying indicators, namely, degree of attention, novelty and centrality. This study enriches method of the research fronts identification based on multi-source data fusion.

Key words: Multi-source data fusion, Carrier fusion, Feature fusion, Relationship fusion, Research fronts identification, Topic model