Page 187 - Journal of Library Science in China, Vol.45, 2019

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186 Journal of Library Science in China, Vol.11, 2019

enhances the external impact and promotes the knowledge contribution.
Although the external impact of Chinese LIS knowledge is enhancing over time, the current
interdisciplinary knowledge export is still limited. To exert stronger knowledge impact, this paper
suggests: Chinese LIS research should develop the core of disciplinary knowledge and improve
research quality and innovative capacity. Additionally, Chinese LIS should broaden its research
horizons, attract the frontiers of global scientific innovation, and improve its interdisciplinarity.

Measuring academic contributions via keyword analytics

Charles C. HUANG, Star X. ZHAO, BIAN Yangyi, Ronda J. ZHANG, Helena H. ZHANG & Fred Y. YE 〇a ∗

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,

* Correspondence should be addressed to Fred Y. Ye, Email: yye@nju.edu.cn, ORCID: 0000-0001-9426-934X