• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.549-566
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• 2022

Recently, artificial intelligence (AI) technology has progressed rapidly, and industries using this technology are significantly increasing. Further, analysis research using text mining, which is an application of artificial intelligence, is being actively developed in the field of social science research. About 125 laws, including joint laws, have been enacted under the Ministry of Oceans and Fisheries in various sectors including marine environment, fisheries, ships, fishing villages, ports, etc. Research on the laws under the Ministry of Oceans and Fisheries has been progressively conducted, and is steadily increasing quantitatively. In this study, the domestic research trends were analyzed through text mining, targeting the research papers related to laws of the Ministry of Oceans and Fisheries. As part of this research method, first, topic modeling which is a type of text mining was performed to identify potential topics. Second, co-occurrence network analysis was performed, focusing on the keywords in the research papers dealing with specific laws to derive the key themes covered. Finally, author network analysis was performed to explore social networks among authors. The results showed that key topics have been changed by period, and subjects were explored by targeting Ship Safety Law, Marine Environment Management Law, Fisheries Law, etc. Furthermore, in this study, core researchers were selected based on author network analysis, and the tendency for joint research performed by authors was identified. Through this study, changes in the topics for research related to the laws of the Ministry of Oceans and Fisheries were identified up to date, and it is expected that future research topics will be further diversified, and there will be growth of quantitative and qualitative research in the field of oceans and fisheries.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.2
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• pp.353-378
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• 2014

The purpose of this study is to find factors to affect on the formation of semantic relation from terminology and what is to be affected by these factors to build the database scheme of terminology dictionary by a structural definition. In this research, 826,905 keywords of 88,874 social science articles and 985,580 keywords of 125,046 humanities science articles in the KCI journals from 2007 to 2011 were collected. From collected data, subject complexity, structural hole, term frequency, occurrence pattern and an effect between the number of nodes and the number of patterns which were derived from the semantic relation of linked terms of established 'STNet' System were analyzed. The summarized results from analyzed data and network patterns are as follows. Betweenness Centrality, term frequency, and effective size affect the numbers of semantic relation node. Among these factors, betweenness centrality was the most effective and effective size. But term frequency was the least effective. Betweenness Centrality, term frequency, and effective size affect the numbers of semantic relation type. Term frequency is the most effective. Therefore, when building a terminology dictionary, factors of betweenness centrality, term frequency, effective size, and complexity of subject are needed to select term. As a result, these factors can be expected to improve the quality of terminology dictionary.

• Information and Communications Magazine
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• v.29 no.7
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• pp.82-88
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• 2012

새로운 정보나 기술 확산에 대한 연구가 소설네트워크의 발전과 함께 많은 관심을 받고 있다. 정보와 기술 확산에는정보/기술의 품질과 함께 얼마나 많은 사람이 정보/기술을 선택했는지도 중요한 영향을 미친다. 동일한 정보/기술을 선택한 사람들 수를 network exteranlity라고 한다. 일반적으로 사람들이 의사결정을 할때에는 본인의 가족,친구등에 의해 많은 영향을 받기 때문에 사회적 관계도 또한 network externality는 단순히 동일한 선택을 한 사람수가 아닌 사회적인 관계가 있은 사람들 안에서 얼마나 많은 사람들이 동일한 선택을 하는지가 중요하게 된다. 이러한 사회적 관계는 그래프로 나타낼 수 있다. 사회적 관계에 의한 영향들은 사람과 사람들간의 local interaction에 의해 결정된다. 본 고에서는 network 상에서 정보/기술이 확산 연구의 전반적인 주제들을 network externality 관점에서 살펴보고, 현재 활발하게 진행중인 연구 주제들을 알아본다.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.3
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• pp.353-371
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• 2011

The purpose of this paper is to analyze the interrelation among research areas based on domestic journal papers, achievements of korea researchers. Generally, the content of papers is appeared through abstracts, subjects, full-text and so on. This paper is focused on subject terms of Domestic journal papers. The experimental data are 80 domestic journals, 7,616 papers and 58,143 subject terms and papers published in 2009. As the result, it was different to use subject terms on each research area: Engineering, Agriculture & Oceanography, Interdisciplinary Science, Social Science, Arts & Physical Education, Medicine & Pharmacology, Humanities and Natural Science. Subject terms of Engineering have used the most in the other research areas in aspect of term co-occurrence. The 8 research areas were grouped in 3 clusters: C1(Engineering, Natural Science, Social Science, Interdisciplinary Science, Humanities), C2(Medicine & Pharmacology, Arts & Physical Education), and C3(Agriculture & Oceanography).

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.91-110
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• 2021

In the face of the prolonged Covid-19, this study conducted a network analysis to propose the policy direction for the Korean National Assembly to go forward. Using COVID-19 news articles, various types of networks were created and analyzed for the parliamentary activities of the Korean National Assembly related to Covid-19. Specifically, we utilize the co-occurrence and keyword information to generate two types of parliamentary networks: co-occurrence-based network and content-based network. In addition, a topic keyword-driven parliamentary network was constructed by using topic modeling. The results of the study are as follows. First, lawmakers in the ruling party had a wide range of topics regarding Covid-19, while lawmakers from other political parties had a limited number of issues covered. Next, a few representative legislators were identified as influential actors in most of the centrality indicators. Based on the research results, cooperation on diverse agendas related to Covid-19 should be promoted between lawmakers from various political parties. And representative legislators from both major parties should play a crucial role as intermediaries to increase communication between them.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.430-440
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• 2021

We analyzed research trends of 288 Korean academic dissertations and articles regarding platform work, using topic modeling and keyword network analysis method. Research disciplines of many studies were laws, business administration, and economics fields. Thigh frequent themes were platform labor protection measures and direct or indirect effects of the sharing economy. The main keywords were digital, value, industry, and labor in terms of infrastructure and structural change. Besides, the main topics were the protection of platform workers, the values of sharing services, digital paradigm, and platform regulations. Based on the results of the analysis, we derived four implications and suggestions such as researching structural frames in macroscopic contexts, generalizing case analysis, and technology supplementation by applying average and quantitative analysis methods, researching individual competency development to realize the essential symbiotic value of sustainability, and developing customized vocational education and training programs.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.7-32
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• 2017

Recently, deep learning has been rapidly spreading as an innovative machine learning technique in various domains. This study explored the research trends of deep learning via modified ego centered topic citation analysis. To do that, a few seed documents were selected from among the retrieved documents with the keyword 'deep learning' from Web of Science, and the related documents were obtained through citation relations. Those papers citing seed documents were set as ego documents reflecting current research in the field of deep learning. Preliminary studies cited frequently in the ego documents were set as the citation identity documents that represents the specific themes in the field of deep learning. For ego documents which are the result of current research activities, some quantitative analysis methods including co-authorship network analysis were performed to identify major countries and research institutes. For the citation identity documents, co-citation analysis was conducted, and key literatures and key research themes were identified by investigating the citation image keywords, which are major keywords those citing the citation identity document clusters. Finally, we proposed and measured the citation growth index which reflects the growth trend of the citation influence on a specific topic, and showed the changes in the leading research themes in the field of deep learning.

• Journal of the Korean Society for information Management
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• v.39 no.4
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• pp.347-373
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• 2022

In this study, in order to expand the access points of reading materials, a reading material classification (RMC) system based on the facets of book use was devised. The facets of books that can be considered by book users in the reading situation were content-analyzed. Also, through network analysis, subject headings adjacent to one subject heading were grouped into related subject headings. The RMC developed in this study can be used as a tool that provides various access points to help book users search in the library OPAC and other reading information systems.

• Korean Association of Arts Management
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• no.49
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• pp.5-35
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• 2019

The main purpose of this study was to use semantic network analysis to examine the international trend of arts management and other studies pertinent to this field. The subject was based on 357 keywords listed on the abstract of 185 research papers in the International Journal of Arts Management. To examine the most current trends of arts management based studies the time frame was restricted from 2008 to 2017. To briefly summarize the result, first, 'museum' was the most frequently appeared keyword. This was followed by 'performing arts' and 'arts' with more than 20 appearances. 'Motion picture industry' and 'theater' were the next frequently appeared keywords. 'Customer behavior' and 'market strategy', keywords related to management, were also included in the high ranked group along with art related keywords. Second, yearly research trend shows that arts management has been regularly studied for past ten years with average of 19 research papers with about 53 keywords. Keywords such as 'museum' and 'performing arts' has been regularly studied for past ten years. 'Culture', 'theater' and 'motion pictures industry' does not regularly appear in the result of yearly research trend but nevertheless they have sparsely made an appearance along the past decade. 'Art gallery' has not been cited till 2011 but from 2012 it was regularly and continuously made an appearance in the yearly research trend. Overall, the yearly trend result shows that the trend of international arts management studies within IJAM, was at first centered on fine arts but as the time passed there has been diversified keywords related to management. Third, 'performing art' and 'art' has the highest link frequency(34). Fourth, density result was 0.039 which shows that the keyword density is not very high. Fifth, 'art', 'performing art', 'museum', 'theater' and 'brand' were positioned in the middle when looking at the visualized version of centrality result. This means that these five keywords has the highest centrality among other keywords.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.177-178
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• 2019

본 연구는 지방교육자치의 가장 핵심인 '교육감' 관련 연구들의 주요 핵심어들과 그들 간의 관계성을 분석하였다. 본 연구에서는 2009년부터 2018년까지(10년간)의 '교육감' 관련 선행연구 총 93건을 키워드 네트워크 분석 방법론을 활용하여, 주요 핵심어 추출 및 워드 클라우드 제시, 주요 핵심어들 간의 관계성(의미망 네트워크) 분석 등을 진행하였다. 최근 10년간 국내 '교육감' 관련 연구들의 주요 핵심어들은 교육감선거, 주민직선제, 선출제도, 개선방안, 비교연구, 교육자치, 문제점, 지방자치, 교육부장관, 교육위원 등 이었다. 주요 핵심어들(상위 출현빈도)은 높은 밀도와 연결정도를 가지고 상호 네트워크를 형성하고 있었다. 본 연구결과는 향후 진행될 '교육감' 관련 후속연구들의 새로운 연구주제 선정 및 다양한 방향 설정에 기초자료로 활용될 수 있을 것이다.