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Detecting the intellectual structure of library and information science based on formal concept analysis

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Abstract

Detecting intellectual structure of a knowledge domain is valuable to track the dynamics of scientific research. Formal concept analysis (FCA) provides a new perspective for knowledge discovery and data mining. In this paper we introduce a FCA-based approach to detect intellectual structure of library and information science (LIS). Our approach relies on the mathematical theory which formulates the understanding of “concept” as a unit of extension (scholars) and intension (keywords) as a way of modelling the intellectual structure of a domain. By analyzing the papers published in sixteen prominent journals of LIS domain from 2001 to 2013, the intellectual structure of LIS in the new century has been identified and visualized. Nine major research themes of LIS were detected together with the core keywords and authors to describe each theme. The significant advantage of our approach is that the mathematical formulae produce a conceptual structure which automatically provides generalization and specialization relationships among the concepts. This provides additional information not available from other methods, especially when shared interests of authors from different granularities are also visualized in concept lattice.

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  1. http://conexp.sourceforge.net/.

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Acknowledgments

This project is supported by National Natural Science Foundation of China (71203164) and the Fundamental Research Funds for the Central Universities (2012GSP058, Wuhan University).

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Authors and Affiliations

  1. School of Information Management, Wuhan University, Wuhan, 430072, People’s Republic of China

    Ping Liu, Qiong Wu, Kaipeng Yu & Yiting Guo

  2. School of Information Studies, University of Wisconsin-Milwaukee, Milwaukee, WI, 53102, USA

    Xiangming Mu

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  1. Ping Liu
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Correspondence to Ping Liu.

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Liu, P., Wu, Q., Mu, X. et al. Detecting the intellectual structure of library and information science based on formal concept analysis. Scientometrics 104, 737–762 (2015). https://doi.org/10.1007/s11192-015-1629-z

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