• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.3
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• pp.247-267
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• 2011

The purpose of this study is to investigate knowledge structure of knowledge organization research area in Korea. The study employed content analysis and network analysis to analyze degree centrality, betweenness, and eigenvector as well as frequency of words. It also analyzes research articles published during the period of 2000-2001. The study can be summarized that the network of keywords of knowledge organization researches is compact and complicated. Cataloging and classification play important roles in the network, and metadata and ontology becomes focal areas in knowledge organization. On the other hand, networks of authorships and authors are broad and fragmented. Collaboration is not active enough.

• Journal of Information Management
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• v.37 no.4
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• pp.25-47
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• 2006

A terminology system capable of providing interpretations and classification information on a multilingual science and technology(S&T) terminology is essential to establish an integrated search environment for multilingual S&T information systems. This paper aims to build a base system to manage an integrated information system for multilingual S&T terminology search. It introduces a method to build a search system for S&T terminologies internally linked through the multilingual semantic network and a search technique on the multiple linked nodes. In order to provide a foundation for further analysis researches, it also attempts to suggest a basic approach to interpret terminology clusters generated with those two search methods.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.421-431
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• 2018

The purpose of this study is to analyze the trends of 'traditional' terminology in Korea. We focus on an empirical investigation of how media reports are conveying 'tradition' terminology in our society by applying text mining and social network analysis techniques. The analysis covered 2,481,143 news articles related to 'tradition' terminology that appeared in the media since the 1920's. In this research, frequency analysis, association analysis and social network analysis were used on articles related to 'tradition' terminology from 1920 to 2017 by decade. By applying these data science techniques, we can grasp the meaning of social culture phenomenon related 'tradition' with objective and value-neutral position and understand the social symbolism which contains the tradition of the times.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.550-555
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• 2018

This study aims to identify and compare contents and keywords used in articles related to blockchain applications to various industries. The text mining and Semantic Network Analysis, as methods of keyword network analysis, were used to analyze articles including terms of 'finance' 'energy' and 'logistics', which media and government frequently mentioned as areas that can apply blockchain technologies. For this study, data were collected from 43,093 articles from January, 2017 through July, 2018. Data crawling was carried out by using Python BeautifulSoup and data cleaning was performed in order to eliminate mutual redundancies of the three terms. After that, text mining and semantic network analysis were performed using Textom and UCInet for network analysis between keywords. The results showed that all the three terms were similar in terms of 'technology', but there were differences in the contents of 'government policy' or 'industry' issues. In addition, there were differences in frequencies and centralities of these terms.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.61-63
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• 2006

본 논문은 온톨로지를 이용하여 단백질 상호작용 네트워크를 개념적으로 분류하여 레이아웃하는 방법을 제안한다. 상호작용 네트워크를 이루는 단백질은 온톨로지의 표준 통제 용어에 대한 주석 정보를 가지고 있으므로 동일 분류에 해당하는 통제 용어를 가지고 있는 단백질들은 근접한 곳에 위치하도록 레이아웃한다. 이는 기존 물리적 레이아웃에 기능별 그룹화를 해줌으로써 복잡한 네트워크를 개념적으로 분석할 수 있도록 한다. 또한, 동일 분류에 속하는 단백질들을 한 노드로 대응하여 레이아웃 알고리즘을 수행함으로써 기존의 그래프표현 알고리즘 보다 빠르게 시각화할 수 있다.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.119-129
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• 2016

With the development of web, amount of information are generated in social web. Then many researchers are focused on the extracting and analyzing social issues from various social data. The proposed approach performed gathering the science data and analyzing with LDA algorithm. It generated the clusters which represent the social topics related to 'health'. As a result, we could deduce the relationship between science data and social issues.

• Journal of Korean Library and Information Science Society
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• v.53 no.3
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• pp.169-191
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• 2022

The purpose of this study is to attempt a text network analysis for two years of 『Reference Materials for Librarian』 (2016-2017) published by the Library Operation Methodology Research Institute in North Korea. A text network analysis can measure how important a particular word by grasping the connectivity and relationship between words beyond a simple word frequency analysis, and it is also possible to interpret specific social phenomena and derive implications. Frequency, degree centrality, the betweenness centrality, community analysis of the collected words were calculated using NetMiner. As a result, the terms 'users', 'information services', 'information needs', 'information technology', 'social learning', 'computers', 'databases', 'information acquisition', 'information retrieval' and 'librarian' were appeared as important ones in understanding North Korean libraries.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.1-28
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• 2021

FinTech has been discussed as an important business area towards technology-driven financial innovation. The term fintech is a combination of finance and technology, which means ICT technology currently associated with all finance areas. The popularity of the fintech industry has significantly increased over time, with full investment and support for numerous startups. Therefore, both academia and practice tried to analyze the trend of the fintech area. Despite the fact, however, previous research has limitations in terms of collecting relevant databases for fintech and identifying proper application areas. In response, this study proposed a new method for analyzing the trend of Fintech fields by expanding Fintech's terminology and using network analysis and topic modeling. A new Fintech terminology list was created and a total of 18,341 patents were collected from USPTO for 10 years. The co-classification analysis and network analysis was conducted to identify the technological trends of patent classification. In addition, topic modeling was conducted to identify the trends of fintech in order to analyze the contents of fintech. This study is expected to help both managers and investors who want to be involved in technology-driven financial services seize new FinTech technology opportunities.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.24-34
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• 2022

Viewing religion as contents, we analyzed the network structure by creating networks on 13 world religions. The whole network was constructed by combining 13 religions, and it showed the characteristics of a scale-free network as a general social network. The world religion network had a very small value of clustering coefficient, unlike the general social network. This seems to be the result of the diversity of terms that describe religion. The core network was constructed by applying K-core algorithm used to create the core network to the whole network. When k-3 was applied, it was too complicated but when k-4 was applied, it was too simple to obtain meaningful results. It indicates that it difficult to apply the K-core algorithm to a network containing a low clustering coefficient. Therefore, core networks were constructed according to the number of key words centered on the hub node to analyze the characteristics of world religions. In addition, meaningful information was derived by constructing the world's five major religious networks and East Asian religious networks. In this study, various information was obtained by analyzing world religions as contents. It was also presented a method of creating and analyzing a core network based on key words for networks with a low clustering coefficient.

• Journal of the Korean Society for information Management
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• v.28 no.2
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• pp.65-78
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• 2011

The purpose of this study is to identify topic areas of academic library research using two informetric methods; word clustering and Pathfinder network. For the data analysis, 139 articles published in major library and information science journals from 2005 to 2009 were collected from the Korean Science Citation Index database. The keywords that represent research topics were gathered from two sections: an and titles in references. Results showed that reference titles usefully represent topics in detail, and combinings and reference titles can produce an expanded topic map.