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主题词组合新颖性与论文学术影响力的关系研究
引用本文:任海英,王德营,王菲菲.主题词组合新颖性与论文学术影响力的关系研究[J].图书情报工作,2017,61(9):87-93.
作者姓名:任海英  王德营  王菲菲
作者单位:1. 北京工业大学经济与管理学院 北京 100124; 2. 北京现代制造业发展研究基地 北京 100124
基金项目: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)和国家社会科学青年基金项目“基于多维信息计量分析的学术影响力综合评价研究”(项目编号:15CTQ023)研究成果之一。
摘    要:目的/意义] 研究学术论文内容的组合新颖性与其学术影响力的关系,为研究论文的学术影响力提供新的研究视角。方法/过程] 采用文本挖掘方法对论文题目、摘要和关键词中的主题词进行提取,通过构建领域主题词共现网络,为每篇论文设计了新颖组合率、中等组合率和常规组合率3个指标,将领域论文划分为不同的新颖性/常规性类型,然后对不同类型论文中高被引论文所占的比例进行统计分析。结果/结论] 同时具有主题词组合高新颖性和高常规性特点的论文成为高被引论文的几率显著高于其他类型的论文,因此建议研究者在科学研究中应注重新颖知识与常规知识的适当组合。

关 键 词:主题词  组合新颖性  被引频次  共现网络  
收稿时间:2017-02-23

Relationship Between Novelty of Key-Term Combinations and Papers' Scientific Impact
Ren Haiying,Wang Deying,Wang Feifei.Relationship Between Novelty of Key-Term Combinations and Papers' Scientific Impact[J].Library and Information Service,2017,61(9):87-93.
Authors:Ren Haiying  Wang Deying  Wang Feifei
Institution:1. Research Base of Beijing Modern Manufacturing Development, Beijing 100124; 2. College of Economics and Management, Beijing University of Technology, Beijing 100124
Abstract:Purpose/significance] We study the combinational novelty of academic papers' contents, providing a new perspective for research in academic impact.Method/process] We apply text mining methods for extracting key-terms in the title, abstract and keywords part of all papers in a particular research field, and design three indicators-novel, medium and conventional combination rates for each paper by constructing a key-term co-occurrence network. Then we divide all papers into different novelty/convention combination types, and analyze the proportion of highly cited papers for different combination types.Result/conclusion] We find that papers of high novelty and high conventionality have a significantly higher probability of becoming highly cited papers than other types of papers. The results imply that researchers should seek appropriate combination of novel and conventional knowledge in the process of scientific research.
Keywords:key-term  combinational novelty  citation frequency  co-occurrence network  
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