A study on construction and analysis of discipline knowledge structure of Chinese LIS based on CSSCI
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This study proposes a method to automatically establish a narrow-sense knowledge structure for Chinese Library and Information Science (CLIS) using data from the Chinese Social Science Citation Index. The method applies multi-level clustering, using ontological ideas as theoretical guidance and ontology learning techniques as technical means. Knowledge categories generated are checked for cohesion and coupling through hierarchical clustering analysis and multidimensional scaling analysis in order to verify the accuracy and rationality of the narrow-sense knowledge structure of CLIS. Finally, the narrow-sense knowledge structure is expanded to a broad sense. Using scholars as objects in examples, this study discusses the semantic associations between topic knowledge and the other academic objects in CLIS from the micro-, meso-, and macro-levels, so as to fully explore the broad-sense knowledge structure of CLIS for knowledge analysis and applications.
KeywordsChinese Library and Information Science (CLIS) Discipline knowledge structure (DKS) Chinese Social Science Citation Index (CSSCI) Multi-level clustering (MLC) Hierarchical clustering analysis (HCA) Multidimensional scaling analysis (MDSA) Social network analysis (SNA)
This work was supported by Jiangsu Province’s Natural Science Foundation Project named “Study on Chinese Ontology Learning-Oriented Patent Forewarning” (No. BK20130587), as well as a major program of the National Social Science Foundation of China named “Studies on Deep Polymerization and Services of Network Information Resource-Oriented Discipline Field” (No. 12&ZD221).
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