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http://dx.doi.org/10.7838/jsebs.2021.26.2.123

A Study on the Analysis of Related Information through the Establishment of the National Core Technology Network: Focused on Display Technology  

Pak, Se Hee (Department of Security Convergence, Graduate School, Chung-Ang University)
Yoon, Won Seok (Department of Security Convergence, Graduate School, Chung-Ang University)
Chang, Hang Bae (Department of Industrial Security, Chung-Ang University)
Publication Information
The Journal of Society for e-Business Studies / v.26, no.2, 2021 , pp. 123-141 More about this Journal
Abstract
As the dependence of technology on the economic structure increases, the importance of National Core Technology is increasing. However, due to the nature of the technology itself, it is difficult to determine the scope of the technology to be protected because the scope of the relation is abstract and information disclosure is limited due to the nature of the National Core Technology. To solve this problem, we propose the most appropriate literature type and method of analysis to distinguish important technologies related to National Core Technology. We conducted a pilot test to apply TF-IDF, and LDA topic modeling, two techniques of text mining analysis for big data analysis, to four types of literature (news, papers, reports, patents) collected with National Core Technology keywords in the field of Display industry. As a result, applying LDA theme modeling to patent data are highly relevant to National Core Technology. Important technologies related to the front and rear industries of displays, including OLEDs and microLEDs, were identified, and the results were visualized as networks to clarify the scope of important technologies associated with National Core Technology. Throughout this study, we have clarified the ambiguity of the scope of association of technologies and overcome the limited information disclosure characteristics of national core technologies.
Keywords
National Core Technology; Scope of Related Technology; Concept Network; TF-IDF(Term Frequency-Inverse Document Frequency); LDA Topic Modeling; Display Technology;
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