• Journal of the Korean Society for Library and Information Science
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• v.55 no.1
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• pp.323-341
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• 2021

Various research are being published in the areas of humanities, history, aviation/ports, and regional development, centering on the Incheon area which has issues such as large-scale ports and airports, archipelago, and urban regeneration. This study explored the scope of the subject and the distribution of researchers using a informetric analysis focusing on the studies of Incheon. Specifically, this study extracted authors from about 500 Incheon-related research papers listed in the Korean journal's citation index and analyzed the co-author relationship network to understand the cooperative behavior between authors' institutions. In addition, by extracting keywords from the articles and performing a weighted network (PFNET) analysis on the relationship between keywords, the intellectual structure was analyzed. As a result, it was found that Inha University and Incheon National University showed a high TBC, and Incheon Development Institute showed the high NNC. Meanwhile, the intellectual structure of Incheon-related research was found to be composed of 11 thematic clusters, and the social issues of Incheon, ports, and aviation were analyzed as representative clusters.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.667-671
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• 2006

Co-Authorship analysis is a kind of bibliometric analysis. and a well established tool for character of knowledge network in some technical fields. If more than two authors co-author an article, there is a relation of knowledge. So This study aims at discovering the trends and phases of OLED technology using co-authorship analysis, a kind of bibliographic analysis. The Science Citation Index Expanded(SCIE) database was used to search for the number of times paired Korean articles were co-authored in OLED field.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.4
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• pp.549-566
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• 2022

Recently, artificial intelligence (AI) technology has progressed rapidly, and industries using this technology are significantly increasing. Further, analysis research using text mining, which is an application of artificial intelligence, is being actively developed in the field of social science research. About 125 laws, including joint laws, have been enacted under the Ministry of Oceans and Fisheries in various sectors including marine environment, fisheries, ships, fishing villages, ports, etc. Research on the laws under the Ministry of Oceans and Fisheries has been progressively conducted, and is steadily increasing quantitatively. In this study, the domestic research trends were analyzed through text mining, targeting the research papers related to laws of the Ministry of Oceans and Fisheries. As part of this research method, first, topic modeling which is a type of text mining was performed to identify potential topics. Second, co-occurrence network analysis was performed, focusing on the keywords in the research papers dealing with specific laws to derive the key themes covered. Finally, author network analysis was performed to explore social networks among authors. The results showed that key topics have been changed by period, and subjects were explored by targeting Ship Safety Law, Marine Environment Management Law, Fisheries Law, etc. Furthermore, in this study, core researchers were selected based on author network analysis, and the tendency for joint research performed by authors was identified. Through this study, changes in the topics for research related to the laws of the Ministry of Oceans and Fisheries were identified up to date, and it is expected that future research topics will be further diversified, and there will be growth of quantitative and qualitative research in the field of oceans and fisheries.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.63-83
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• 2016

As the usage of information resources produced in various media and forms has been increased, the importance of metadata as a tool of information organization to describe the information resources becomes increasingly crucial. The purposes of this study are to analyze and to demonstrate the intellectual structure in the field of metadata through co-word analysis. The data set was collected from the journals which were registered in the Core collection of Web of Science citation database during the period from January 1, 1998 to July 8, 2016. Among them, the bibliographic data from 727 journals was collected using Topic category search with the query word 'metadata'. From 727 journal articles, 410 journals with author keywords were selected and after data preprocessing, 1,137 author keywords were extracted. Finally, a total of 37 final keywords which had more than 6 frequency were selected for analysis. In order to demonstrate the intellectual structure of metadata field, network analysis was conducted. As a result, 2 domains and 9 clusters were derived, and intellectual relations among keywords from metadata field were visualized, and proposed keywords with high global centrality and local centrality. Six clusters from cluster analysis were shown in the map of multidimensional scaling, and the knowledge structure was proposed based on the correlations among each keywords. The results of this study are expected to help to understand the intellectual structure of metadata field through visualization and to guide directions in new approaches of metadata related studies.

• Journal of the Korean Society for information Management
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• v.35 no.2
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• pp.89-114
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• 2018

Research on changes in research trends in academic disciplines is a method that enables observation of not only the detailed research subject and structure of the field but also the state of change in the flow of time. Therefore, in this study, in order to observe the changes of research trend in library and information science field in Korea, co-word analysis was conducted with Korean author keywords from three types of journals which were listed in the Korea Citation Index(KCI) and have top citation impact factor were selected. For the time series analysis, the 15-year research period was accumulated in 5-years units, and divided into 2003~2007, 2003~2012, and 2003~2017. The keywords which limited to the frequency of appearance 10 or more, respectively, were analyzed and visualized. As a result of the analysis, during the period from 2003 to 2007, the intellectual structure composed with 25 keywords and 8 areas was confirmed, and during the period from 2003 to 2012, the structure composed by 3 areas 17 sub-areas with 76 keywords was confirmed. Also, the intellectual structure during the period from 2003 to 2017 was crowded into 6 areas 32 consisting of a total of 132 keywords. As a result of comprehensive period analysis, in the field of library and information science in Korea, over the past 15 years, new keywords have been added for each period, and detailed topics have also been subdivided and gradually segmented and expanded.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.23-50
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• 2022

Source memory is a cognitive process that combines the representation of the origin of the episodic experience with an item. By studying this daily process, researchers have made fundamental discoveries that make up the foundation of brain and behavior research, such as executive function and binding. In this paper, we review and conduct a bibliometric analysis on source memory papers published from 1989 to 2020. This review is based on keyword co-occurrence networks and author citation networks, providing an in-depth overview of the development of source memory research and future directions. This bibliometric analysis discovers a change in the research trends: while research prior to 2010 focused on individuality of source memory as a cognitive function, more recent papers focus more on the implication of source memory as it pertains to connectivity between disparate brain regions and to social neuroscience. Keyword network analysis shows that aging and executive function are continued topics of interest, although frameworks in which they are viewed have shifted to include developmental psychology and meta memory. The use of theories and models provided by source memory research seem essential for the future development of cognitive enhancement tools within and outside of the field of Psychology.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.309-330
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• 2022

Intellectual structure analysis, which quantitatively identifies the structure, characteristics, and sub-domains of fields, has rapidly increased in recent years. Analysis techniques traditionally used to conduct intellectual structure analysis research include bibliographic coupling analysis, co-citation analysis, co-occurrence analysis, and author bibliographic coupling analysis. This study proposes a novel intellectual structure analysis method, Keyword Bibliographic Coupling Analysis (KBCA). The Keyword Bibliographic Coupling Analysis (KBCA) is a variation of the author bibliographic coupling analysis, which targets keywords instead of authors. It calculates the number of references shared by two keywords to the degree of coupling between the two keywords. A set of 1,366 articles in the field of 'Open Data' searched in the Web of Science were collected using the proposed KBCA technique. A total of 63 keywords that appeared more than 7 times, extracted from 1,366 article sets, were selected as core keywords in the open data field. The intellectual structure presented by the KBCA technique with 63 key keywords identified the main areas of open government and open science and 10 sub-areas. On the other hand, the intellectual structure network of co-occurrence word analysis was found to be insufficient in the overall structure and detailed domain structure. This result can be considered because the KBCA sufficiently measures the relationship between keywords using the degree of bibliographic coupling.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.141-157
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• 2021

In this study, we aimed to understand research trends of domestic artificial intelligence research. To achieve the goal, we applied network analysis and dynamic topic modeling to domestic research papers on artificial intelligence. Among the papers that have been indexed in KCI (Korean Journal of Citation Index) by 2020, metadata and abstracts of 2,552 papers where the titles or indexed keywords include 'artificial intelligence' both in Korean and English were collected. Keyword, affiliation, subject field, and abstract were extracted and preprocessed for further analyses. We identified main keywords in the field by analyzing keyword co-occurrence networks as well as the degree and characteristics of research collaboration between domestic and foreign institutions and between industry and university by analyzing institutional collaboration networks. Dynamic topic modeling was performed on 1845 abstracts written in Korean, and 13 topics were obtained from the labeling process. This study broadens the understanding of domestic artificial intelligence research by identifying research trends through dynamic topic modeling from abstracts as well as the degree and characteristics of research collaboration through institutional collaboration networks from author affiliation information. In addition, the results of this study can be used by governmental institutions for making policies in accordance with artificial intelligence era.