• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.141-157
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• 2021

In this study, we aimed to understand research trends of domestic artificial intelligence research. To achieve the goal, we applied network analysis and dynamic topic modeling to domestic research papers on artificial intelligence. Among the papers that have been indexed in KCI (Korean Journal of Citation Index) by 2020, metadata and abstracts of 2,552 papers where the titles or indexed keywords include 'artificial intelligence' both in Korean and English were collected. Keyword, affiliation, subject field, and abstract were extracted and preprocessed for further analyses. We identified main keywords in the field by analyzing keyword co-occurrence networks as well as the degree and characteristics of research collaboration between domestic and foreign institutions and between industry and university by analyzing institutional collaboration networks. Dynamic topic modeling was performed on 1845 abstracts written in Korean, and 13 topics were obtained from the labeling process. This study broadens the understanding of domestic artificial intelligence research by identifying research trends through dynamic topic modeling from abstracts as well as the degree and characteristics of research collaboration through institutional collaboration networks from author affiliation information. In addition, the results of this study can be used by governmental institutions for making policies in accordance with artificial intelligence era.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.68-73
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• 2016

This study was conducted to identify trends and knowledge structures associated with recent trends in Korean tourism from 2010 to 2015 using keyword data. To accomplish this, we constructed a network using keywords extracted from KCI journals. We then made a matrix describing the relationships between rows as papers and columns as keywords. A keyword network showed the connectivity of papers that have included one or more of the same keywords. Major keywords were then extracted using the cosine similarity between co-occurring keywords and components were analyzed to understand research trends and knowledge structure. The results revealed that subjects of tourism research have changed rapidly and variously. A few topics related to 'organization-employee' were major trends for several years, but intrinsic and extrinsic factors have been further subdivided and employees of specific fields have been targeted as subjects of research. Component analysis is useful for analyzing concrete research topics and the relationships between them. The results of this study will be useful for researchers attempting to identify new topics.

• International Commerce and Information Review
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• v.19 no.1
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• pp.23-42
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• 2017

International Commerce and Information Review has been playing an important role of disseminating the outstanding research results in the fields such as trade information and systems, e-trade, regional studies, e-commerce, service trade, trade laws since 1999. This paper aims to find the research trends and distinguished characteristics in the field of trade information by analyzing research keywords of the research papers published in this journal using a social network analysis method. Research keyword data collected from the homepage of the academic society were cleaned and transformed into the co-occurrence network data, which are suitable for social network analysis. NodeXL Pro was used to analyze and visualize the pre-processed data. Through clustering analysis, the most important subject fields or interests were identified as well as those which worked as intermediaries for interdisciplinary researches.

• Spatial Information Research
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• v.23 no.1
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• pp.69-77
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• 2015

The aim of this study is to examine the research trends of global scientific production of Geospatial Information (GI) papers from 1998 to 2013 by using keyword network analysis. This study constructed keyword network model through papers and keywords related to GI research retrieved from the Web of Science DB and performed keyword network analysis such as Degree Centrality, Betweenness Centrality, and Closeness Centrality. The results show that GI has been steadily applied to various fields, and also the research trends of GI techniques could be quantitatively characterized through keyword network analysis. This study result can be applied to establish the policies and the national R&D planning of geospatial information.

• Electronics and Telecommunications Trends
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• v.23 no.2 s.110
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• pp.153-161
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• 2008

최근 웹의 성공을 토대로 기존의 공개 소프트웨어 제공 방식을 벗어나, 언제 어디서나 누구라도 쉽게 소프트웨어와 콘텐츠를 사용하고 제작하고 유통할 수 있는 방향으로 기술이 발전하고 있다. 여기에 소프트웨어나 콘텐츠를 특정 서버에 의한 중앙 집중적인 방식으로 관리하지 않고, 개인이 이동식 저장 장치를 통해 휴대하면서 어떠한 단말기에서도 동일한 작업 환경에서 관리할 수 있는 기술이 요구되고 있다. 이러한 이동형 소프트웨어 플랫폼 관련 기술은 아직 명확히 형성되어 있지 않은 새로운 기술 분야로서 SaaS, 웹, 이동형 소프트웨어라는 세 가지 기술 키워드를 토대로 접근할 수 있다. 본 고에서는 웹 서비스, 온디맨드 컴퓨팅 등과 같은 기존의 서버 중심 기술 외에도 위의 세가지 키워드를 포괄하도록 여덟 가지 세부기술 분야로 분류하여 특허를 검색한 후 국가, 기술 분야, 주요 출원인별 특허 동향을 연도별 추이와 함께 정리한 후, 세부기술 분야별 동향도 분석하였다.

• Journal of Korean Society of Archives and Records Management
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• v.23 no.4
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• pp.73-89
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• 2023

This study aims to analyze the frequency of keywords used in Korean abstracts, which are unstructured text data in the domestic record management research field, using text mining techniques to identify domestic record management research trends through distance analysis between keywords. To this end, 1,157 keywords of 77,578 journals were visualized by extracting 1,157 articles from 7 journal types (28 types) searched by major category (complex study) and middle category (literature informatics) from the institutional statistics (registered site, candidate site) of the Korean Citation Index (KCI). Analysis of t-Distributed Stochastic Neighbor Embedding (t-SNE) and Scattertext using Word2vec was performed. As a result of the analysis, first, it was confirmed that keywords such as "record management" (889 times), "analysis" (888 times), "archive" (742 times), "record" (562 times), and "utilization" (449 times) were treated as significant topics by researchers. Second, Word2vec analysis generated vector representations between keywords, and similarity distances were investigated and visualized using t-SNE and Scattertext. In the visualization results, the research area for record management was divided into two groups, with keywords such as "archiving," "national record management," "standardization," "official documents," and "record management systems" occurring frequently in the first group (past). On the other hand, keywords such as "community," "data," "record information service," "online," and "digital archives" in the second group (current) were garnering substantial focus.

• Journal of Industrial Convergence
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• v.18 no.2
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• pp.45-50
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• 2020

The purpose of this study is to analyze relationships among essential keywords in curriculum. The number of 1,935 keyword was collected from 644 manuscripts published between 2002 and 2019. For data analysis, this study selected softwares of KrKwic and KrTitle to compose a 1-mode network matrix and UCINET 6 and NetDraw to implement network analysis and visualization. Results are as follows. First, the frequency of keyword was curriculum, curriculum development, national curriculum, competency-based curriculum, 2015 revised national curriculum, curriculum implementation, understanding by design, competency, teacher education, school curriculum, and IBDP from highest to lowest. Second, degree centrality was curriculum development, curriculum, competency-based curriculum, national curriculum, 2015 revised national curriculum, understanding by design, competency, key competency, high school curriculum, textbook, curriculum implementation, teacher education, and IBDP from highest to lowest.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.71-96
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• 2023

This study aimed to investigate key research areas in the field of Library and Information Science (LIS) by analyzing trends and identifying emerging topics. To facilitate the research, a collection of 40,897 author keywords from 11,252 papers published in the past 30 years (1993-2022) in five journals was gathered. In addition, keyword analysis, as well as Principal Component Analysis (PCA) and correlation analysis were conducted, utilizing variables such as the number of articles, number of authors, ratio of co-authored papers, and cited counts. The findings of the study suggest that two topics are likely to develop as promising research areas in LIS in the future: machine learning/algorithm and research impact. Furthermore, it is anticipated that future research will focus on topics such as social media and big data, natural language processing, research trends, and research assessment, as they are expected to emerge as prominent areas of study.

• Journal of the Korean Society for information Management
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• v.35 no.2
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• pp.89-114
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• 2018

Research on changes in research trends in academic disciplines is a method that enables observation of not only the detailed research subject and structure of the field but also the state of change in the flow of time. Therefore, in this study, in order to observe the changes of research trend in library and information science field in Korea, co-word analysis was conducted with Korean author keywords from three types of journals which were listed in the Korea Citation Index(KCI) and have top citation impact factor were selected. For the time series analysis, the 15-year research period was accumulated in 5-years units, and divided into 2003~2007, 2003~2012, and 2003~2017. The keywords which limited to the frequency of appearance 10 or more, respectively, were analyzed and visualized. As a result of the analysis, during the period from 2003 to 2007, the intellectual structure composed with 25 keywords and 8 areas was confirmed, and during the period from 2003 to 2012, the structure composed by 3 areas 17 sub-areas with 76 keywords was confirmed. Also, the intellectual structure during the period from 2003 to 2017 was crowded into 6 areas 32 consisting of a total of 132 keywords. As a result of comprehensive period analysis, in the field of library and information science in Korea, over the past 15 years, new keywords have been added for each period, and detailed topics have also been subdivided and gradually segmented and expanded.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.21-34
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• 2023

The purpose of this study is to examine the extent of Sustainable Development Goals (SDGs)-related research in the field of entrepreneurship globally since the adoption of the SDGs at the UN General Assembly, and to compare international and domestic research trends in order to determine the direction of SDGs-related research in entrepreneurship in Korea. Utilizing three databases-Web of Science (WoS), KCI, and DBpia- SDGs-related studies in entrepreneurship were extracted by employing specific search terms. After data purification, a total of 356 studies abroad and 4 studies in Korea were used for analysis. After data purification, a total of 356 international studies and 4 Korean studies were analyzed. Due to the limited number of domestic studies, the research trends were examined by conducting frequency analysis and keyword network analysis on international studies alone. Frequency analysis revealed that SDGs research in entrepreneurship primarily focused on sustainability-related terms and was conducted in conjunction with business models, innovation, entrepreneurship education, and strategies. Furthermore, yearly frequency analysis demonstrated an expansion of topics to encompass research on entrepreneurship and SDGs policies, the roles and capabilities of female entrepreneurs in SDGs implementation, energy start-ups and SDGs, directions for implementing SDGs in business schools and SDGs education, indicators for SDGs implementation and evaluation, and technologies for sustainability. The keyword network analysis identified central topics such as business, sustainability, SDGs, innovation, entrepreneurship, business models, and education, with research areas extending to entrepreneurship ecosystems, change and strategy, ethics, and climate. This study holds significance in establishing a foundation for SDGs research in entrepreneurship, which is currently an underexplored area in Korea, by presenting emerging research trends related to SDGs in entrepreneurship.