| 黄晨,赵星,卞杨奕,张家榕,张慧,叶鹰.测量学术贡献的关键词分析法探析[J].中国图书馆学报,2019,45(6):84~99 |
| Measuring Academic Contributions via Keyword Analytics |
| 测量学术贡献的关键词分析法探析 |
| Received:August 06, 2019 |
| DOI： |
| Key words: Keyword analytics Keyword vector Keyword flow Keyword flux Mainstream ratio Mainstream index Academic evaluation |
| 中文关键词: 关键词分析法 关键词向量 关键词流量 关键词通量 主流率 主流指数 学术评价 |
| 基金项目: |
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| Abstract: |
As a tool of logical thinking and derivation, the basic element of language is the word. Important words reflect concepts, and core concepts construct knowledge, while knowledge evolution contributes to academic development. Based on the theoretical foundation that keywords in the academic literature characterize concepts, this work attempts to get beyond traditional citation analysis and introduce a framework of academic contribution analysis based on keywords.
The framework of keyword analytics defines keyword vector flux spectrum and cumulative flux spectrum based on the keyword vector and keyword flow in a discipline, and then applies h index and g index to measure h cutoff and g cutoff, which constitute the mainstream keyword set in h core and g core. We then define the mainstream ratio and mainstream index, which can be used to measure the contribution of academic subjects (e.g. institutions) or academic objects (e.g. journals) to mainstream academic research.
The method of keyword analytics for measuring academic contributions inherits the theoretical characteristics of h type metrics, and provides a new measurement in addition to citation analytics. In the field of humanities, the traditional quantitative evaluation methods have many limitations, and the keyword analysis method is worthy to explore. This method framework still has limitations and needs further innovation and development. For example, a series of conceptual clues such as keyword, theme word, concept word and ontology, are currently confused and need to be clarified. While using keyword analytics, the techniques of front end word processing and back end text mining still need to be further explored. On the basis of semantics, how to measure synonyms, near synonyms, substitute words, and evolution of keyword itself accurately, is also a problem and needs further exploration.
The empirical data in this article is derived from the data platform built by the authors. The platform consists of three Chinese databases, CNKI, Wanfang and Weipu, and Chinese records in WoS and Ei. The data is cleaned and processed by machine plus manual work. The platform includes 87 million Chinese achievement data and 17 million foreign language achievement data, totaling 104 million, with a time span of 2008 to 2017. In this study, two data subsets on library and information science and philosophy were respectively selected as empirical cases. 5 figs. 5 tabs. 23 refs. |
| 中文摘要: |
| 语言作为逻辑思维和推理工具,其基本要素是语词。重要语词映射成概念,核心概念建构知识,而知识演进促成学术发展。本文以学术文献中的 |
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