智識建構方法論之改進研究

黃馨儀

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智識建構方法論之改進研究

Improving the Intellectual Structure Methodology – An Experimental Study using Multiple Document Corpora

黃馨儀 , Masters　　Advisor：陳宗天　　

繁體中文

一致性檢定；期望值最大化；智識建構； Expectation Maximization ； Intellectual Structure ； Coherence

Abstract │ Reference (53) │ Times Cited (2) │ Altmetrics

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Intellectual Structure (IS) is a method that has been widely applied in knowledge domain analysis and science mapping. Factor analysis is often applied in the Intellectual Structure methods to reduce the dimension of the data by ascribing multiple documents to single factors. Each factor generally represents one research theme. The Pearson correlation coefficient matrix, which is derived from the factor analysis, is used to construct the intellectual structure diagram that facilitates knowledge domain visualization. However, factor analysis assumes the input data is normally distributed, which is an untested premise.  An article may not be assigned to any factor due to its low factor loading. In order to amend the issues of untested normal distribution premise and unassigned articles, we replace the factor analysis method with Expectation Maximization to carry out several experiments and compare the results.

The results of our experiment provide empirical evidence that the EM-based intellectual structure method generates more coherent document clusters than the conventional one. With the supporting results on hand, we can improve the intellectual structure methodology by using Expectation Maximization to replace the conventionally used factor analysis.

Reference ( 53 ) 〈TOP〉

Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389-2404.
連結：
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1–7), 107-117.
連結：
Chen, C. (2006). Information Visualization: Beyond the Horizon: Springer-Verlag New York, Inc.
連結：
Chen, T. T. (2012). The development and empirical study of a literature review aiding system. Scientometrics, 92(1), 105-116. doi: 10.1007/s11192-012-0728-3
連結：
Chen, T. T. (2015). The congruity between linkage-based factors and content-based clusters—an experimental study using multiple document corpora. Journal of the Association for Information Science and Technology, n/a-n/a. doi: 10.1002/asi.23413
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