• Proceedings of the Korean Society for Information Management Conference
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• 2014.08a
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• pp.119-122
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• 2014

본 연구는 교육학 분야에서 국내 학술 논문 저자키워드 출현 순서에 의미가 있는지 알아보기 위하여 저자키워드 출현 순서별로 빈도를 조사하고 동 저자 키워드가 제목에 출현하는지를 살펴본 후, 학술논문 저자키워드 출현 순서에 따른 빈도가 저자키워드의 논문 제목 출현 빈도에 미치는 영향에 대해 연구하고자 한다. 연구의 배경은 구조적 학술용어사전의 구축과 관련해서 효율적인 용어 선정이나 용어 분류 방법을 찾는 것이다. 연구의 목적을 위해 교육학 분야 저자키워드의 출현 빈도수가 1인 것을 제외하고 고빈도 순으로 400개 용어를 추출하였다. 저자키워드의 몇 번째 출현 순서가 저자키워드의 제목 출현에 가장 큰 영향을 미치는지 알아본 결과, 저자키워드의 첫 번째 출현이 아닌 두 번째 출현이 가장 큰 영향을 미친다는 것을 알 수 있었다.

• Journal of the Korean Society for information Management
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• v.31 no.2
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• pp.121-142
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• 2014

This study aims to investigate the importance of author keyword with analysis the position of author keyword in journal. In the first stage, an analysis was carried out on the position of author keyword. We examined the importance of author keyword by using degree centrality, closeness centrality, betweenness centrality, eigenvector centrality and effective size of structural hole. In the next stage, We performed analysis on correlation between network centrality measures and the position of author keyword. The result of correlation analysis on network centrality measures and the position of author keyword shows that there are the more significant areas of the result of the correlation analysis on degree centrality, betweenness centrality and the position of keyword. In addition, These results show that we need to consider that the possible way as measuring the importance of author keyword in journal is not only a term frequency but also degree centrality and betweenness centrality.

• Journal of the Korea Convergence Society
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• v.9 no.9
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• pp.9-14
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• 2018

This study aims to investigate an importance of Author keyword with analysis the position of author keyword. An analysis was carried out on the position of author keyword. we examined an importance of Author keyword by using degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. In the next stage, we performed analysis on correlation between network centrality measures and the position of keyword. As a result, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality both has a high value in 4th author keyword order. eigenvector centrality was the comparatively effective method to separate of author keyword order method than other 3 centrality. Correlation analysis result shows that the network analysis value are increasing in order. This study has significance in that it was able to examine the author keyword behavior. Future research is needed to identify and supplement future situational factors, behavior, and psychology.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.225-246
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• 2012

This study purported to investigate the possibility of automatic descriptor assignment using the reclassification of author keywords in domestic scholarly databases. In the first stage, we selected optimal classifiers and parameters for the reclassification by comparing the characteristics of machine learning classifiers. In the next stage, learning the author keywords that were assigned to the selected articles on readings, the author keywords were automatically added to another set of relevant articles. We examined whether the author keyword reclassifications had the effect of vocabulary control just as descriptors collocate the documents on the same topic. The results showed the author keyword reclassification had the capability of the automatic descriptor assignment.

• Journal of the Korean Society for information Management
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• v.30 no.2
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• pp.227-243
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• 2013

The purpose of this study is to verify the correlation between the appearance frequency of author keyword and the number of citation in journal articles. In this study, we were trying to develop a methodology that can select the term having semantic relation with other terms and higher utilization to build a structured scientific glossary. In order to achieve this purpose, we analyzed the number of citation and the author keyword of the humanities and social science journal articles of the Korea Citation Index (KCI) from 2007 to 2011. This study found a correlation between appearance frequency of author keyword and the number of citation of the journal articles, with higher appearance frequency of author keyword of the journal articles being more cited.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.53-54
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• 2014

대부분 국내에서 발행되는 과학기술 분야의 논문에는 저자 키워드가 포함되어 있다. 이 키워드는 논문을 이해를 돕고 온라인 검색에 유용하게 활용되고 있다. 특히 많은 논문에서 키워드를 영문과 국문을 동시에 부여하도록 하고 있어 과학기술 용어로서의 가치도 있다. 일정 기간 국내에서 발행되는 논문으로부터 저자 키워드들을 추출하여 다양한 각도에서 부여 키워드의 현황을 분석하였다. 결론으로 바람직한 키워드 부여의 방향을 제시한다.

• Proceedings of the Korean Society for Information Management Conference
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• 2010.08a
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• pp.21-28
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• 2010

본 연구에서는 PubMed에 등재되었으나 Medical Subject Headings(MeSH)가 부여되지 않은 국내 의학학술지의 문헌을 대상으로 미국국립의학도서관 (NLM: National Library of Medicine)의 Medical Text Indexer(MTI)를 활용하여 MeSH 용어를 추천받은 후, PubMed 레코드의 유사주제문헌 (Relation Citations, PRC)에 부여된 MeSH와의 일치여부를 분석하였다. 또한 논문의 저자가 부여한 키워드(저자키워드)와 PRC MeSH의 일치여부도 비교하였다. PRC MeSH와 MTI MeSH 추천어의 일치율은 주표목이 21.1%였고, 체크태그는 18.1%, 부표목은 16.5%로 나타났다. 우리나라 의학논문에 나타난 저자키워드의 중요한 특징은 MeSH 주표목 위주이고, 체크태그와 부표목은 거의 사용하지 않는 것이다. 따라서 저자키워드와 PRC MeSH 주표목과의 일치율은 23.4%에 이르지만, 체크태그와 부표목의 일치율은 각각 1%, 2.1%였다. 색인전문가가 통제어휘를 사용하여 색인하는 과정에서 PRC와 MTI의 MeSH 주표목과 저자키워드가 일치하는 용어를 주표목으로 부여하고, PRC와 MTI가 추천하는 체크태그와 부표목을 활용하는 등 국내 의학문헌의 MeSH 용어 부여 작업을 반자동화(semi-indexing)하면, 정확하고 신속한 MeSH 부여 작업이 가능할 것이다.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.141-146
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• 2018

The authors' author keywords are the most important elements that characterize the contents of the paper, By analyzing this in real time and providing it to users, It is possible to grasp research trends. Unstructured data of a journal created in a paper is constructed as a database, make use of this to make index data structure that can search in real time. In the index data structure, a thesis containing a specific keyword is searched, By extracting and clustering the author keywords, By presenting to the user a word cloud that can be displayed by size according to the weight, designed a method to visualize research trends. We also present the results of the research trend analysis of the keywords "virus" and "iris recognition" in the implemented system.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.3
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• pp.383-402
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• 2016

The purpose of this study is to study library marketing research trends in Korea and other countries through the analysis of author keyword network of peer-reviewed journal articles. The author keyword was collected from four major LIS journals in Korea and Scopus academic database for other countries'. The data was analyzed using NetMiner4 software. The results of the study were as follows: 1) In Korea, lots of library marketing studies focused on public libraries. However, there was a range of library marketing researches focused on academic libraries in other countries, 2) In Korea, there was not a variety of subjects of library marketing studies and the studies were mainly led by a few scholars, 3) In other countries, many scholars paid attention to digital library marketing through social media and/or web, and 4) there little library marketing studies focused on school libraries both in Korea and other countries.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.23-31
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• 2003

In general, people use a key word or a phrase as the name of field or subject word in document. This paper has focused on keyword extraction. First of all, we investigate that an author suggests keywords that are not occurred as contents words in literature, and present generation rules to combine compound keywords based on concept of lexical information. Moreover, we present a new importance measurement to avoid useless keywords that are not related to documents' contents. To verify the validity of extraction result, we collect titles and abstracts from research papers about natural language and/or voice processing studies, and obtain the 96% precision in a top rank of extraction result.