• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.54-56
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• 2012

최근 다양한 관점에서의 사회관계망 분석이 확대되면서 R&D분야도 연구자 간의 네트워크를 이해하고자 하는 요구사항도 증가하고 있지만 국내에서는 연구자나 과학자 사이의 네트워크 분석 및 서비스에 대한 시도가 다양하지 않은 상황이다. 본 논문은 국내 R&D분야의 사회관계망에 대한 이해증진을 위하여 구현한 공저자 네트워크 가시화 서비스에 대한 기술이다. 사회 관계망 분석과 서비스를 위하여 동명이인 저자를 식별하고 이들 간의 공저자 관계를 계량정보 분석기법을 이용하여 분석하였으며 연구자들 간의 네트워크를 쉽게 조망할 수 있도록 정보를 가시화 하였다. 본 논문에서 구현한 공저자 네트워크 서비스는 국내 연구자들 간의 협업형태를 직관적으로 이해할 수 있도록 하며 각 연구 분야의 핵심연구자 및 다양한 연구 분야를 연계하는 허브 연구자의 확인을 용이하게 한다.

• Journal of Korean Library and Information Science Society
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• v.50 no.2
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• pp.121-147
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• 2019

The purpose of this study is to investigate the perceptions of the organizations in the fields of academics, arts, humanities, and music in Korea on the International Standard Name Identifier (ISNI) which is developed by International Standard Organization (ISO) for assuring the public identities of creators, contributors, and producers to contents in the fields. National Library of Korea (NLK), one of the official registration agencies of ISNI in Korea, launched a consortium, ISNI-Korea, with various organizations in the fields to promote the ISNI registration and application. In this study, focused group interviews were carried out with participants from a total of 13 organizations; these organizations were divided into three groups by the progress on collaboration with NLK at the ISNI-Korea consortium. The current status of the personnel or membership information management by organizations, their expectations and barriers for using ISNI, and the ISNI application to the fields have been discussed intensively during the interviews. Findings from this study could benefit NLK to develop the strategies to promote ISNI in the fields. This study also proposed follow-up studies to enhance the use of ISNI in Korea.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.1
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• pp.247-262
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• 2011

Although research has shown a significant amount of work identifying various dimensions of relevance along with exhaustive lists of relevance criteria, there seem to have been less effort to apply the findings to improve actual systems design. Based on this assumption, this paper investigates to what extent those relevance criteria have been incorporated into the interface features of major commercial Web search engines, suggesting what can/should be done more. Before stepping into the actual system features, this paper compares recent relevance research in Information Science with other human factor studies both in Information Science and its neighboring discipline (HCI), as an attempt to identify studies that are conceptually similar to the relevance research, but not named as such way. Similarities and differences between these studies are presented. Recommendations suggested to support applicable interface features include: 1) further personalization of interface designs; 2) author-supplied meta tags for the Web contents; and 3) extensions of beyond-topical representations based on link structure.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.389-396
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• 2024

Generative AI has recently been utilized across all fields, achieving expert-level advancements in deep data analysis. However, identifying regional names in scientific literature remains a challenge due to insufficient training data and limited AI application. This study developed a standardized dataset for effectively classifying regional names using address data from Korean institution-affiliated authors listed in the Web of Science. It tested and evaluated the applicability of machine learning and deep learning models in real-world problems. The BERT model showed superior performance, with a precision of 98.41%, recall of 98.2%, and F1 score of 98.31% for metropolitan areas, and a precision of 91.79%, recall of 88.32%, and F1 score of 89.54% for city classifications. These findings offer a valuable data foundation for future research on regional R&D status, researcher mobility, collaboration status, and so on.

• Journal of the Korean Society for information Management
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• v.29 no.1
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• pp.157-174
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• 2012

This paper compares two recent methods for exploring a scientist's research history: citation identity and self-citation network. The former is proposed by White(2000), while the latter is suggested by Hellsten et al.(2007). An experimental citation analysis was carried out on the research output of Young Mee Chung, a renouned Korean information scientist. The result shows that the two methods divided the research period into two sub-periods in the same way. They also identified the major research themes very similarly. In the analysis of each method's performance in depth, the two methods revealed different functions to understand a researcher's history. Citation identity was useful to identify authors who have affected Chung's research in terms of research topics. whereas, self-citation network was successful to identify the core papers and leading papers of the research sub-periods. This study indicates the combination of two methods can provide rich information on a scientist's research history.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.129-136
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• 2015

We investigate the underlying structure of big data research domain, which is diversified and complicated using bottom-up approach. For that purpose, we derive a set of articles by searching "big data" through the Korea Citation Index System provided by National Research Foundation of Korea. With some preprocessing on the author-provided keywords, we analyze bibliometric data such as author-provided keywords, publication year, author, and journal characteristics. From the analysis, we both identify major sub-domains of big data research area and discover the hidden issues which made big data complex. Major keywords identified include SOCIAL NETWORK ANALYSIS, HADOOP, MAPREDUCE, PERSONAL INFORMATION POLICY/PROTECTION/PRIVATE INFORMATION, CLOUD COMPUTING, VISUALIZATION, and DATA MINING. We finally suggest missing research themes to make big data a sustainable management innovation and convergence medium.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.127-140
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• 2022

With the recent development of the ICT (information and communication technology), the revolution of the industry has moved on from the third industry to the fourth. There is no doubt that the companies would not survive in the future without adopting these technologies. The purpose of this research is to analyze the intellectual structure of the internet of things(IoT) literature for the Industry 4.0 to suggest a better insight for the field. The data for this research is extracted from the Web of Science database. Total of 1,631 documents and 72,754 references are used for the research with the analysis program CiteSpace. Author co-citation analysis is used to analyze the intellectual structure and performed clustering, timeline and burst detection analysis. We identified 12 sub-areas of IoT for the Industry 4.0 which are 'Supply Chain', 'Digital Twin', 'Smart Manufacturing System' and etc. Through the timeline analysis we can find out which clusters will increase or decrease its reputation. As concluding remarks, limitations and further research suggestions are discussed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1755-1759
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• 2004

Digital watermarking technology is one of method of protecting property from the illegal reproduction of digital data. This technology inserts the specific data in a certain file to identify the property, that is an author and rights, not for human to be seen and heard. In this paper, the watermarking technology which inserts a RGB color watermark in a JPEG2000 color image using the visual characteristics of wavelet coefficient was proposed. After applying various attack at a watermarked image according to proposed technology, the likeness between the original image, the watermark and the extracted watermark was measured and investigated. As a result, the PSNR value of image was varied depending on perceptual parameter, but we can obtain 32dB as a whole.

• Journal of the Korean Society for information Management
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• v.29 no.2
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• pp.45-70
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• 2012

There have been many methods and algorithms proposed for multidimensional scaling to mapping the relationships between data objects into low dimensional space. But traditional techniques, such as PROXSCAL or ALSCAL, were found not effective for visualizing the proximities between objects and the structure of clusters of large data sets have more than 50 objects. The CLUSCAL(CLUster-oriented SCALing) technique introduced in this paper differs from them especially in that it uses cluster structure of input data set. The CLUSCAL procedure was tested and evaluated on two data sets, one is 50 authors co-citation data and the other is 85 words co-occurrence data. The results can be regarded as promising the usefulness of CLUSCAL method especially in identifying clusters on MDS maps.

• Journal of Korean Library and Information Science Society
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• v.46 no.1
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• pp.1-23
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• 2015

The purpose of this paper is to development an algorithm for FRBR implementation(functional requirements for bibliographic records), analyzing KORMARC bibliographic records by work types. For this purpose, it was utilized analyzing home and foreign case studies including OCLC and LC's algorithm. Analyzing the experimental data from the Korean National Library's bibliographic records, it was extracted from identifying elements by FRBR's four bibliographic entities. To cluster a related works by work-set, the algorithm was designed to construct the authorized access points as a combination of an author name and a title from the record. I suggested that it should be wholly improved a quality of existing bibliographic records and level of data input to development a FRBR algorithm in Korean libraries.