• Journal of the Korean BIBLIA Society for library and Information Science
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• v.26 no.4
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• pp.245-262
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• 2015

As the topics of general education in universities become more diverse, it is not an easy task to identify the topics of general education courses. This study aims to identify and visualize the topics of A university's general education courses using informetric analysis methods. 214 syllabi were collected and titles, course introduction, goals, and weekly plans were analyzed. 278 topic words were extracted from the data set and grouped into 8 clusters. In the network analysis, topic clusters were divided into two areas, personal and social. Personal area has 14 sub-topic clusters and social area has 11 sub-topic clusters. In personal area, 'language', 'science', and 'personality' were major topic clusters. In social area, 'multi-culture' cluster was the core cluster with connected to four other clusters. The topic network generated in this study can be used for the university and the university library to enhance general education or to develop collections for general education.

• Journal of the Korean Society for information Management
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• v.29 no.4
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• pp.295-312
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• 2012

This research aims to present the ego-centered topic citation analysis, which is a new application of White's ego-centered citation analysis, for analyzing multilayered knowledge structure of a subject domain. An experimental topic citation analysis was carried out on the folksonomy research documents retrieved from Web of Science. Ego-centered topic citation analyses on folksonomy research domain were conducted in three stages: ego-documents set analysis, topic citation identity analysis, and topic citation image analysis. The results showed that the ego-centered topic citation analysis suggested in this study was successfully performed to illustrate the inner and the outer knowledge structures of folksonomy research domain.

• Journal of the Korean Society for information Management
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• v.30 no.1
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• pp.237-258
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• 2013

As topics of researchers become diverse horizontally or vertically, academic libraries have difficulties to identify the dynamic change of researchers' needs for academic publications. This research aims to illustrate the topic areas of researchers in a department of university by analyzing bibliographies of their publications. First, researchers' publications were used to discover the topic areas where the researchers had published. Second, the cited publications in those papers were analysed to identify the expanded topic areas of these researchers. Finally, highly cited journals were analyzed by network analysis method. The major finding is that the importance of topic areas by the number of journals was not necessarily proportional to that by the number of papers. Researchers have a tendency to use many papers in a small number of journals in a certain topic area. Furthermore, the importance of topic areas discovered by researchers' publications was not the same as that discovered by researchers' citations.

• Proceedings of the Korean Society for Information Management Conference
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• 2013.08a
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• pp.43-46
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• 2013

본 연구에서는 국내 문헌정보학 분야의 연구 논문 중 이용자 관련 연구 논문 125편을 대상으로 논문에 부여된 주제어간의 연결 관계를 분석 하였다. 사전 작업을 통하여 정리된 226개의 주제어에 대한 연결 관계를 네트워크 분석을 통하여 분석하고 시각화 하였다. 그래프를 통하여 주제어간 연결 강도를 확인하였고, 다른 주제어와 연결성이 높은 상위 20개의 주제어를 제시하였다. 주제어간 근접성이 높은 주제어를 군집화한 결과 14개의 군집으로 정리되었다. 다른 주제어와 연결이 없이 고립된 군집이 8개, 연결된 군집이 6개였다.

• Journal of the Korean Society for information Management
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• v.28 no.1
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• pp.171-193
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• 2011

This study aims to analyse the necessity of the subject heading construction for the word based classification and to suggest a methodology that uses various knowledge organization systems(KOS). For this purpose, six kinds of KOS were collected for the 20 selected works in each subject. The collected subjects were analysed in terms of constructing a subject heading for the word based classification. The result of the analysis shows that there is a noticeable difference between the library oriented KOS and commercial oriented KOS. In addition, user oriented tags are more similar to the commercial sector's concerning subject categorization than the library oriented ones. However, there is no noticeable difference among the library oriented KOS, commercial sector oriented KOS, and user oriented tags regarding the subject vocabulary. Some practical implications were suggested for the application to the Korean libraries based on the findings of this study.

• Proceedings of the Korean Society for Information Management Conference
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• 2013.08a
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• pp.51-54
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• 2013

본 연구는 정보학 분야의 저명한 연구자 T.D.Wilson의 연구주제 분야를 네트워크 분석을 이용하여 해석해보고, 그의 연구는 물론 정보학 분야의 연구주제에 관한 이해를 도모하는 데 연구의 목적이 있다. 이를 위해 그의 저작을 대상으로 서지결합분석 방법을 이용한 군집 분석을 실시하여 연구주제를 나누어 보고 대표적인 연구주제와 논문, 그리고 인용빈도와의 관계를 규명하였다. 패스파인더 네트워크와 노드엑셀을 이용한 분석 결과, 대표적인 연구주제는 정보행위연구이고 논문으로는 "Human information behavior(2000)"로 나타났다. 더불어 '정보요구'라는 핵심 연구주제 아래 정보탐색, 정보관리, 정보이용, 웹정보에 이르는 정보학 분야의 다양한 연구가 이루어졌음을 알 수 있다.

• Journal of Korean Library and Information Science Society
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• v.42 no.3
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• pp.331-350
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• 2011

This study compared and analyzed, after extracting the subject titles related to Korea and Japan from the second version of Chinese Classified Thesaurus, the number of titles and characteristics according to the subjects. The analysis result shows that total number of Korea-related titles including proper nouns was 215, which is limited in comparison to that of Japan, in terms of the number and diversity of the subjects. Particularly, the CCT does not accurately reflect the current state of Korea as it uses the word 'Josun' to denote Korea and calls Korean War 'Josun War' as well as only recording it in North Korean history. Meanwhile, Japan-related subject titles include many that show the complicated historical relationship between Japan and China, such as Manchurian Incident and Japan-China War.

• Journal of Korean Library and Information Science Society
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• v.48 no.4
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• pp.21-47
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• 2017

This study aims to analyze features of topics about AI(Artificial Intelligence) which is gaining a massive attention these days. Newspaper articles published from 2016 to June, 2017 were selected to analyze key subjects. The reason why the period was selected is people started to get attention on AI since 2016 as AlphaGo came out and gave a shock. The number of coded main message was 1,210 in 525 newspaper articles in total. The messages were categorized as three subject categories: the seven major categories, 62 middle categories. and minor categories. The seven major categories contains issues such as AI research, AI application, AI business, AI era, AI argument, AlphaGo, and other topics. The first features of issues about AI found in the major subject categories is that they are various and complicate. Second, it is important that social and policy-level issues related AI, such as job losses, misuse, and error should be dealt with to utilize AI safely. Last, issues related the role of human and revolution of education system in the AI era were shown as subjects which are important but hard to discuss.

• Journal of the Korean Society for Library and Information Science
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• v.42 no.4
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• pp.533-549
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• 2008

A job analysis of subject specialist librarians is an important source providing information about definition of a job, development of a curriculum, administration of human resources, criteria of employee training, and deciding institutional and individual objectives. This study analyzes a job of subject specialist librarians in Korea, and divides their jobs into 7 duties and 58 tasks. the 7 duties are as follows; A. a development of subject information resources, B. a management of information resources classified by subjects, C. a research support service classified by subjects, D. Liasion activity of library users, E. an education of subject classification for library users, F. library management, G. personal development of classified subject areas.

• Journal of Korean Library and Information Science Society
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• v.47 no.4
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• pp.1-18
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• 2016

The purpose of this study is to explore application of topic modeling for topic analysis of book report. Topic modeling can be understood as one method of topic analysis. This analysis was conducted with texts in 23 book reports using LDA function of the "topicmodels" package provided by R. According to the result of topic modeling, 16 topics were extracted. The topic network was constructed by the relation between the topics and keywords, and the book report network was constructed by the relation between book report cases and topics. Next, Centrality analysis was conducted targeting the topic network and book report network. The result of this study is following these. First, 16 topics are shown as network which has one component. In other words, 16 topics are interrelated. Second, book report was divided into 2 groups, book reports with high centrality and book reports with low centrality. The former group has similarities with others, the latter group has differences with others in aspect of the topics of book reports. The result of topic modeling is useful to identify book reports' topics combining with network analysis.