• Journal of Convergence for Information Technology
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• v.9 no.2
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• pp.7-16
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• 2019

The purpose of this study is to identify the characteristics of researches that have been conducted for the last 20 years through analyzing global research trends and evolutions of design articles from 1999 to 2018 with keyword network analysis. For this purpose, we selected 3,569 articles in 22 journals related to design research retrieved from the Scopus database and constructed keyword network model through the author keyword and index keyword. The frequency of the author and index keyword, the centrality of betweenness and degree were analyzed with the keyword network. The results show that design has been applied to various fields for recent 20 years, and the research trends of design could be quantitatively characterized by keyword network analysis. The result of this study could be used to suggest future research topics in the field of design based on quantitative and empirical data.

• Science of Emotion and Sensibility
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• v.23 no.4
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• pp.61-72
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• 2020

Social problems, such as economic instability, aging population, heightened competition, and changes in personal values, might become more serious in the near future. Affective computing has received much attention in the scholarly community as a possible solution to potential social problems. Accordingly, we examined domestic and global knowledge structure, major keywords, current research status, international research collaboration, and network for each major keyword, focusing on keywords related to affective computing. We searched for articles on a specialized academic database (Scopus) using major keywords and carried out bibliometric and network analyses. We found that China and the United States (U.S.) have been active in producing knowledge on affective computing, whereas South Korea lags well behind at around 10%. Major keywords surrounding affective computing include computing, processing, affective analysis, research, user modeling categorizing recognitions, and psychological analysis. In terms of international research collaboration structure, China and the U.S. form the largest cluster, whereas other countries like the United Kingdom, Germany, Switzerland, Spain, and Canada have been strong collaborators as well. Contrastingly, South Korea's research has not been diverse and has not been very successful in producing research outcomes. For the advancement of affective computing research in South Korea, the present study suggests strengthening international collaboration with major countries, including the U.S. and China and diversifying its research partners.

• 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.

• Proceedings of the Korean Society for Information Management Conference
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• 2012.08a
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• pp.143-146
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• 2012

본 연구에서는 MEDLINE에 수록된 논문의 암 관련 우울증 문헌을 대상으로 MeSH(Medical Subject Headings) 키워드를 이용하여 1992년부터 2011년까지 20년간의 연구 동향을 분석하였다. 암과 우울증에 해당하는 MeSH 키워드를 주제로 다룬 논문 3,389편과 50,778개의 키워드를 대상으로 주요 학술지 및 암 발병 위치와 우울증 치료요법을 분석하였다. 암 관련 우울증 논문의 암 발병 위치별 빈도는 유방암이 799편으로 가장 높았으며, 폐암, 전립선암, 뇌종양, 두경부암이 뒤를 이었다. 또한 우울증 치료 요법별 빈도는 비약물치료가 552편으로 약물치료 400편보다 높게 나타났으며 비약물치료는 크게 상담치료와 상담 외 치료에 대한 키워드로 구분되었고, 약물치료는 치료 요법명과 약명으로 다시 구분되었다.

• 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.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.511-518
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• 2012

In this study, the trend in green technology was observed and the domain of the green technology area that will be actively studied in the future was found by establishing knowledge map in green technology area and comparing and analyzing green technology information in Korea and overseas in time series. For the purpose of this study, network analysis was conducted for the keyword of green technology information provided by green technology information portal site (www.gtnet.go.kr) operated by Korea Institute of Science and Technology Information. Network analysis was conducted using keyword, and change of study subject was found by dividing the analysis result into periods. In the result of network analysis on top 100 keywords from total English keyword, it was found that renewable energy related areas such as solar energy and biomass had high centrality. When the main keyword trend by year was studied, centrality of solar cell, nanotechnology, smart grid, and fuel cell were found to increase, showing that research and development in generation and use of renewable energy are actively made.

• Knowledge Management Research
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• v.24 no.4
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• pp.1-22
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• 2023

Knowledge management can be defined as the valuable storing and creation of new knowledge, as well as the sharing of this knowledge to be applied in all areas of an organization's management activities (Turban et al., 2003). In a knowledge-based society where intangible intellectual assets are the source of competitive advantage rather than tangible assets, knowledge management activities are emphasized in both academia and industry. This study analyzes research on "knowledge management" keyword indexed in the Korean Citation Index (https://kci.go.kr), operated by the National Research Foundation of Korea (NRF), to identify related research trends in Korea and suggest future directions for knowledge management activities. The results show that knowledge management is being researched through the integration of various fields and theories, indicating the potential for expanding research topics and fostering interdisciplinary collaboration. Furthermore, the study of knowledge management often include the keyword 'innovation', emphasizing its significant role in organizational and technological innovations. The analysis of keywords by year also reveals that they reflect the major environmental changes of each period, demonstrating the increasing importance of knowledge management in the era of the Fourth Industrial Revolution.

• Annual Conference of KIPS
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• 2018.10a
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• pp.463-466
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• 2018

본 연구는 논문을 기반으로 연구의 주요 키워드를 추출하는 알고리즘을 설계하고 이를 적용한 연구 검색 시스템을 개발하여 효율적인 검색 환경을 제공하는 것을 목표로 한다. 논문 키워드 추출 알고리즘은 논문 내에서의 단어 출현 빈도와 PMI 지표를 바탕으로 정의한 단어간 연관성 K(x,y)을 기반으로 설계하였다. 연구 검색 시스템은 고등학교 R&E 등 제한적인 환경에서 이루어지는 연구들의 선행 연구 자료 부족을 해결하는 것을 주 목적으로 한다. 또한, 구현한 연구 검색 시스템에 제안된 알고리즘을 적용하여 보다 정확하고 직관적인 검색 환경을 제공할 수 있었으며, 추후 연구 자료가 추가됨에 따라 그 가치가 높아질 것으로 전망한다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.583-586
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• 2023

대량 문자서비스를 통한 스팸 문자가 계속 증가하면서 이로 인해 도박, 불법대출 등의 광고성 스팸 문자에 의한 피해가 지속되고 있다. 이러한 문제점을 해결하기 위해 다양한 방법들이 연구되어 왔지만 기존의 방법들은 주로 사전 정의된 키워드나 자주 나오는 단어의 출현 빈도수를 기반으로 스팸 문자를 검출한다. 이는 광고성 문자들이 시스템에서 자동으로 필터링 되는 것을 회피하기 위해 비정상 문자를 조합하여 스팸 문자의 주요 키워드를 의도적으로 변형해 표현하는 경우에는 탐지가 어렵다는 한계가 있다. 따라서, 본 논문에서는 이러한 문제점을 해결하기 위해 딥러닝 기반 객체 탐지 및 OCR 기술을 활용하여 스팸 문자에 사용된 변형된 문자열을 정상 문자열로 복원하고, 변환된 정상 문자열을 문장 수준 이해를 기반으로 하는 자연어 처리 모델을 이용해 스팸 문자 콘텐츠를 분류하는 방법을 제안한다. 그리고 기존 스팸 필터링 시스템에 가장 많이 사용되는 키워드 기반 필터링, 나이브 베이즈를 적용한 방식과의 비교를 통해 성능 향상이 이루어짐을 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.504-505
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• 2017

Existing morpheme analyzers classify the words used in writing documents. A system for extracting sentences and paragraphs based on a morpheme analyzer is being developed. However, there are very few systems that compress documents and extract important paragraphs. The algorithm proposed in this paper calculates the weights of the keyword written in the document and extracts the paragraphs containing the keyword. Users can reduce the time to understand the document by reading the paragraphs containing the keyword without reading the entire document. In addition, since the number of extracted paragraphs differs according to the number of keyword used in the search, the user can search various patterns compared to the existing system.