图书情报工作 ›› 2018, Vol. 62 ›› Issue (13): 92-102.DOI: 10.13266/j.issn.0252-3116.2018.13.012

• 知识组织 • 上一篇    下一篇

一种层次化的科学知识结构发现方法

李慧, 田亚丹   

  1. 西安电子科技大学经济与管理学院 西安 710126
  • 收稿日期:2018-01-17 修回日期:2018-04-01 出版日期:2018-07-05 发布日期:2018-07-05
  • 通讯作者: 田亚丹(ORCID:0000-0001-8506-8271),硕士研究生,通讯作者,E-mail:179075482@qq.com
  • 作者简介:李慧(ORCID:0000-0002-3468-5170),副教授,博士,硕士生导师;。
  • 基金资助:
    本文系国家自然科学青年基金项目"基于可信语义Wiki的知识库构建方法与应用研究基金"(项目编号:71203173)、国家自然科学青年基金项目"大规模动态社交网络社团检测算法研究"(项目编号:71401130)和中央高校基本科研业务费专项资金资助项目"大数据环境下基于主题模型的信息服务研究"(项目编号:JB160606)研究成果之一。

A Hierarchical Discovery Method of Scientific Knowledge Structure

Li Hui, Tian Yadan   

  1. School of Economic & Management, Xidian University, Xi'an 710126
  • Received:2018-01-17 Revised:2018-04-01 Online:2018-07-05 Published:2018-07-05

摘要: [目的/意义]提出一种新的层次化科学知识结构发现方法,为优化知识结构发现过程,改善知识组织形式提供借鉴。[方法/过程]利用LDA主题模型构建层次化的科学知识结构发现方法,依据主题间平均相似性自动确定知识结构层数,通过在"文档-主题"概率矩阵中自动筛选阈值截取各主题文献子集,最后采用树形图展示科学领域的知识结构,发掘知识间的关联性和继承性,并与层次主题模型HLDA方法进行比较。[结果/结论]通过实证研究与对比,证明本文提出的方法得到的知识结构更优,知识主题表征性更强且运行效率更高,并在单层主题区分度和层间主题继承性方面较HLDA方法有较大提升。

关键词: LDA, 云计算, 层次化, 知识结构

Abstract: [Purpose/significance] This paper proposes a new hierarchical discovery method of scientific knowledge structure, which provides reference for optimizing knowledge structure discovery process and improving knowledge organization form.[Method/process] Firstly, this paper constructed a hierarchical discovery method of scientific knowledge structure by using LDA topic model. Then, according to the average similarity degree among topics, it automatically determined the hierarchy of knowledge structure, and the literature subsets were intersected by filtering threshold automatically in the "document-topic" probability matrix. Finally, it adopted tree diagram to display the science knowledge structure and explore the correlation and inheritance of knowledge points. Besides, we also compared our method with HLDA method which is a hierarchical topic model.[Result/conclusion] The result shows that the knowledge structure obtained by our method is better, the representation of knowledge topic is stronger and it has the higher operation efficiency. In addition, compared with the HLDA method, our method has a great improvement on the topic differences of the single layer and the topic inheritance between layers.

Key words: LDA, cloud computing, hierarchical, knowledge structure

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