• Journal of Elementary Mathematics Education in Korea
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• v.23 no.4
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• pp.459-479
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• 2019

The purpose of this study is to explore the research trends and knowledge structures of 『Journal of Elementary Mathematics Education in Korea』 by applying the keyword network analysis. To do this, we analyzed the frequency of the occurrence of keywords in the journal and conducted keyword network analysis using the Krkwic program and NodeXL program. The results of the analysis are as follows. Firstly, 749 keywords were extracted from keyword cleansing process and 48 keywords, including mathematics curriculum, mathematics textbooks, school mathematics, mathematical problem solving, mathematically gifted student, etc. appeared more than five times. Secondly, the keyword network analysis showed that the keywords-mathematics textbooks, school mathematics, mathematical problem solving, mathematical communications-have high connection centrality. Finally, we provided the limitations of this study and suggested future research.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.462-465
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• 2021

본 연구는 최근 ICT분야 R&D 동향을 파악하기 위하여 NTIS에서 제공하는 국가연구개발사업 과제정보를 텍스트 마이닝 기법을 통해 분석하였다. 2017년부터 2020까지의 과제 정보에서 키워드를 추출하고 연결 관계 마이닝을 통해 키워드 네트워크를 시각화하였다. 분석 결과는 다음과 같다. 첫째, 정보통신 각 분야에서 핵심 연구주제가 기술의 발전에 따라 변화하고 있음을 관찰하였다. 둘째, 키워드 네트워크 상에서 허브 역할을 하는 키워드를 통해 분야 간 융합의 매개 기술을 파악할 수 있었다. 마지막으로, 연도별 키워드 네트워크를 비교·분석함으로써 새롭게 등장하거나 연결 상태의 변화를 보이는 이머징(Emerging) 키워드를 통해 미래 유망 기술이나 최신 연구 방향성을 감지할 수 있음을 보였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.133-134
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• 2021

본 연구는 최근 고등학교기관(대학)의 평가에서 가장 중심 단어가 되고 있는 있는 '핵심역량' 관련 최근 연구들의 주요 키워드들과 그들간의 네트워크를 분석하고자 한다. 본 연구에서는 2011년부터 2020년까지(최근 10년간)의 '대학 핵심역량' 관련 등재지(등재 후보지 포함)에 발표된 총 176건의 관련 연구물들을 언어 네트워크 분석 방법론을 활용하여, 주요 키워드 추출 및 워드클라우드 제시, 주요 핵심어들 간의 관계성(의미망 네트워크) 분석 등을 진행하고자 한다. 이와 같은 연구 결과는 관련 학자들이 연구를 진행할 때, 대학 관계자가 학교단위 교육활동 계획 기획 및 평가활동을 할 때 매우 중요한 기초 자료로 활용될 것으로 기대된다.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.107-115
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• 2013

Various networks can be observed in the world. Knowledge networks which are closely related with technology and research are especially important because these networks help us understand how knowledge is produced. Therefore, many studies regarding knowledge networks have been conducted. The assortativity coefficient represents the tendency of connections between nodes having a similar property as figures. The relevant characteristics of the assortativity coefficient help us understand how corresponding technologies have evolved in the keyword-based patent network which is considered to be a knowledge network. The relationships of keywords in a knowledge network where a node is depicted as a keyword show the structure of the technology development process. In this paper, we suggest two hypotheses basedon the previous research indicating that there exist core nodes in the keyword network and we conduct assortativity analysis to verify the hypotheses. First, the patents network based on the keyword represents disassortativity over time. Through our assortativity analysis, it is confirmed that the knowledge network shows disassortativity as the network evolves. Second, as the keyword-based patents network becomes disassortavie, clustering coefficients become lower. As the result of this hypothesis, weconfirm the clustering coefficient also becomes lower as the assortative coefficient of the network gets lower. Another interesting result concerning the second hypothesis is that, when the knowledge network is disassorativie, the tendency of decreasing of the clustering coefficient is much higher than when the network is assortative.

• Journal of Korean Library and Information Science Society
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• v.54 no.4
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• pp.393-414
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• 2023

The goal of this study is to investigate domestic research trend on research data study. To achieve this goal, articles related research data topic were collected from RISS. After data cleansing, 134 author keywords were extracted from a total of 58 articles and keyword network analysis was performed. As a result, first, the number of studies related to research data in Korea is still only 58, so it was found that many related studies need to be conducted in the future. Second, most research fields related to research data were focused on library and information science among complex studies. Third, as a result of frequency analysis of author keywords related to research data, 'research data management', 'research data sharing', 'data repository', and 'open science' were analyzed as major frequent keywords, so research data-related research focuses on the above keywords. The keyword network analysis results also showed that high-frequency keywords occupy a central position in degree centrality and betweenness centrality and are located as core keywords in related studies. Through the results of this study, we were able to identify trends related to recent research data and identify areas that require intensive research in the future.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1273-1284
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• 2016

In this paper, we analyzed the patents on machine learning using keyword network analysis and clique analysis. To construct a keyword network, important keywords were extracted based on the TF-IDF weight and their association, and network structure analysis and clique analysis was performed. Density and clustering coefficient of the patent keyword network are low, which shows that patent keywords on machine learning are weakly connected with each other. It is because the important patents on machine learning are mainly registered in the application system of machine learning rather thant machine learning techniques. Also, our results of clique analysis showed that the keywords found by cliques in 2005 patents are the subjects such as newsmaker verification, product forecasting, virus detection, biomarkers, and workflow management, while those in 2015 patents contain the subjects such as digital imaging, payment card, calling system, mammogram system, price prediction, etc. The clique analysis can be used not only for identifying specialized subjects, but also for search keywords in patent search systems.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.101-126
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• 2016

This paper identifies core research topics and their relationships by deriving the research topic networks in the technology management field using co-word analysis. Contrary to the conventional approach in which undirected networks are constructed based on normalized co-occurrence frequency, this study analyzes directed networks of keywords by employing the confidence index of association rule mining for pairs of keywords. Author keywords included in 2,456 articles published in nine international journals of technology management in 2011~2014 are extracted and categorized into three types: THEME, METHOD, and FIELD. One-mode networks for each type of keywords are constructed to identify core research keywords and their interrelationships with each type. We then derive the two-mode networks composed of different two types of keywords, THEME-METHOD and THEME-FIELD, to explore which methods or fields are frequently employed or studied for each theme. The findings of this study are expected to be fruitfully referred for researchers in the field of technology management to grasp research trends and set the future research directions.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.311-314
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• 2018

최근 '미투운동'이 활발히 진행되면서 새로운 페미니즘의 물결을 맞이하였다. 이전의 페미니즘 운동과의 차이점은 SNS 를 통해 익명으로 활동하며 전파속도가 굉장히 빠르다는 것이다. 본 연구는 미투운동의 이러한 특성을 고려하여 실제 트위터 데이터에서 주요 키워드를 파악하고, 해당 키워드의 연관성 및 네트워크 분석으로 사회적 맥락을 알아본다.

• Journal of the Korean Society of Earth Science Education
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• v.11 no.2
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• pp.145-157
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• 2018

The purpose of this study is to analyze the research trends in domestic related to gifted counseling by utilizing Sementic analysis methods. For papers of gifted education in korea, KCI(Korea Citation Index) rated journals were selected 83 pieces published in journals were collected and the Sementic Network Analysis(SNA) way was utilizing for keyword frequency and Centrality Network Analysis throughout a variety of research articles using krkwic and Ucinet6.0. The results are as follows. first, the analysis appeared that the trends of paper keywords from highest frequency of appearance keyword in papers focused on four keywords: perfectionism, career, counseling, and the science gifted. second, Analysis of annual trends from 2001 to June 2018 showed that the top keywords were as follows: the gifted underachievers, the perfectionism, the gifted students of Science, and the science gifted students. the rising keywords were perfectionism, twice-exceptional students, and gifted parents, and the keywords of gifted students and general students showed a tendency to decrease. Consequently, gifted counseling research should be done from various perspectives.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.2
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• pp.85-108
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• 2024

This study aimed to analyze the research trends of research data academic papers from 2010 to 2024 to understand the research status of research data over the past 15 years. To achieve this goal, keyword frequency analysis and network centrality analysis were conducted on 14,921 academic articles published in Scopus DB. The keyword network analysis using UCINET, which was divided into the first period (2010-2014), second period (2015-2019), and third period (2020-2024) according to the period of publication of academic journals, revealed the main keywords studied regardless of the period, the keywords that attracted attention by period, and the keywords that decreased in attention over time. It was found that the most active topic of research data-related research in the last 15 years is data sharing, and most of the keywords with high Degree Centrality also have high Betweenness Centrality. The results of this study can be utilized as a basis for suggesting future research directions in the field of research data in Korea.