图书情报工作 ›› 2016, Vol. 60 ›› Issue (4): 115-124.DOI: 10.13266/j.issn.0252-3116.2016.04.016

• 知识组织 • 上一篇    下一篇

科技论文微观概念地图的构建及研究思路的挖掘

任海英, 石彤   

  1. 北京工业大学经济与管理学院 朝阳 100124
  • 收稿日期:2016-01-04 修回日期:2016-02-06 出版日期:2016-02-20 发布日期:2016-02-20
  • 作者简介:任海英(ORCID:0000-0002-1197-6709),副教授,博士,E-mail:renhaiying@bjut.edu.cn;石彤(ORCID:0000-0002-0459-2301),硕士研究生。
  • 基金资助:
    本文系北京市自然科学基金预探索项目"发明过程和机理的概念地图表示研究"(项目编号:9153020)和北京市教委社科计划面上项目"一种基于概念地图的发明过程机理的描述方法"(项目编号:SM201510005001)研究成果之一。

The Construction of Microscopic Concept and the Mining of Research Ideas in Scientific Papers

Ren Haiying, Shi Tong   

  1. School of Economics and Management, Beijing University of Technology, Beijing 100124
  • Received:2016-01-04 Revised:2016-02-06 Online:2016-02-20 Published:2016-02-20

摘要: [目的/意义] 探索从科技论文中挖掘出作者研究思路的可能性和技术手段,从而高效地获得新的研究创意。[方法/过程] 提出一种从单篇科技论文中抽取概念地图的方法,通过构建其微观概念地图(MCM)来形象地描述作者在研究中重视并运用的知识结构,通过对概念及其关系的定量分析来推测作者的研究重点和创新思路。[结果/结论] 选取一篇发表在2014年Science期刊上的关于聚类方法的论文,展示其MCM的抽取及论文研究思路的挖掘过程,验证所提方法的有效性。

关键词: 概念地图, 研究思路, 自然语言处理, 文本挖掘

Abstract: [Purpose/significance] This paper explores the feasibility and techniques for mining an author's research ideas from his/her technical paper, so readers can obtain new insights efficiently. [Method/process] This paper aims at generating a new and valuable research idea efficiently in the research process. This paper proposes a text mining method for extracting a microscopic concept map (MCM) from a single scientific paper. This MCM visually describes the main knowledge structure of the author used in the paper. Then, through quantitative analysis of the concepts and their relationships in MCM, one can mine the author's research focus and the source of his new research ideas. [Result/conclusion] This paper selects a scientific paper about clustering method which published in Science in 2014,then extracts the MCM of the paper and shows the mining process of the research ideas, to verify the effectiveness of this method.

Key words: concept map, research ideas, natural language processing, text mining

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