• Herbal Formula Science
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• v.18 no.1
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• pp.57-77
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• 2010

Objective : This study reviews the recent trend of oriental medical prescription research. The data examined are the articles published in the Korean Journal of Oriental Medical Prescription from 1990 to 2009. Method : The data are retrieved through the internet database Oriental Medicine Advanced Searching Integrated System (OASIS) and the collection of the Korean Journal of Oriental Medical Prescription. The number of articles examined is 385, published in 25 volumes of the journal. This study examines the nature of the articles, research methods, subjects, and author information. Research subjects are sorted out by the OASIS key words for the articles published before 1999, and by key word indexes cited in the abstracts for the articles published sinceafter. Results : Among the 385 articles collected, 206 are research articles, 143 philological articles, 35 case studies, and 1 special contribution. A majority of research articles are experimental studies (199 articles or 96.6%), while clinical reports (5 articles or 2.43%), and others studies (2 articles) occupy a small portion. Most of experimental studies (183 articles or 91%) examine the effectiveness of certain prescriptions or treatments. Among the effectiveness studies, 114 articles (62.3%) employ in vivo experiment design, 52 articles (28.42%) in vitro experiments, and 17 articles (9.29%) both in vivo and in vitro experiments. In terms of research subject, the most frequently indexed key words are hepatotoxicity among diseases (9 articles), Bojungikgitang (Bu-Zhong-Yi-Qi-Tang) among prescriptions (10 articles), Buja (Acontii Tuber) among meteria medica (4 articles), immunity and anti-oxidation among efficacy terminology (6 articles each), and Donguibogam(東醫寶鑑) among references in the key words (25 articles). Universities are the main affiliation of authors (76.42%), followed by university hospitals (6.71%), non-academic research institutes (5.55%), local clinics (4.67), academic research institutes (2.81%), hospitals (2.38%), and others (1.44%). The most affiliated institute of the first and correspondent authors is Wonkwang University. In terms of authorship, co-authorship outnumbers sole-authorship by 82.08% to 17.92%. The proportion of authors of a single article is 63.54% which is near the author productivity distribution described by Lotka's law.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.4
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• pp.203-230
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• 2010

This study analyzed the development and growth of folksonomy by examining tags associated with 40 bestsellers on LibraryThing.com in 6-month intervals. It was found that tag values do not decrease but grow in terms of quantity and quality. Accordingly, we examined the major significances of the tags and their potential utilization as an expression of subjects. Our findings were as follows. First, the motivations for tagging can be categorized into personal information for search purposes, self-fulfillment such as sense of achievement, display of emotion and sharing of one's experience with others, or an altruistic objective that emphasizes sociality with a desire that one's actions might provide social benefits. According to our analysis, 74.12% of tags had a social motivation. Second, the total number of tags and the frequency of usage increased with time. Third, the categories that showed a high increase in tag usage were dates of publication and reading, key words, main characters, and book reviews. Tags related to subjects had the highest ratio. Fourth, among Library of Congress Subject Headings (LCSH), multiple genres, key words and main characters were assigned to books, and specific key words and other properties were added as time progressed. There was also a slight increase in the number of tags consistent with LCSH. Fifth, we found that key tags could serve as a compilation of terms that reflects the knowledge base of the corresponding era. Thus, folksonomy should be continuously monitored for its quantitative and qualitative development of the tags to make improvements on its formative disadvantages, and identify internal semantic significance, be actively utilized in conjunction with taxonomy as a flexible compilation of terms that incorporate the history of a specific era.

• Journal of agricultural medicine and community health
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• v.46 no.3
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• pp.153-161
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• 2021

Backgrounds: This study was performed to analyze the main key words of newspaper articles related to COVID-19 in 2020 for each category of quarantine measures according to the epidemic period of COVID-19. Methods: We analyzed articles related to COVID-19 in three major newspapers of Korea between February 17 and December 31, 2020. We targeted the front page articles on mondays and thursdays. The analysis of the relationship between the two variables was confirmed through the chi-square test. Results: As a result of analyzing the main key words for each category of quarantine measures, non-pharmaceutical intervention were the most common at 54.3%, followed by 3Ts(test, tracing, treatment and vaccine) at 31.9%. In the category of non-pharmaceutical intervention, social distancing was the most common at 33.9%. In the categories such as 3Ts(test, tracing, treatment) and vaccine, diagnostic tests were the most common at 41.8%. Conclusions: It was identified that non-pharmaceutical intervention were the most common, and there was a difference in the reporting of main key words by category of quarantine measures for each epidemic period related to COVID-19 in 2020.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.79-98
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• 2018

The purpose of this study is to suggest a model analysing spatio-temporal characteristics of the civil complaints for the officially assessed land price based on big data mining. Specifically, in this study, the underlying reasons for the civil complaints were found from the spatio-temporal perspectives, rather than the institutional factors, and a model was suggested monitoring a trend of the occurrence of such complaints. The official documents of 6,481 civil complaints for the officially assessed land price in the district of Jung-gu of Incheon Metropolitan City over the period from 2006 to 2015 along with their temporal and spatial poperties were collected and used for the analysis. Frequencies of major key words were examined by using a text mining method. Correlations among mafor key words were studied through the social network analysis. By calculating term frequency(TF) and term frequency-inverse document frequency(TF-IDF), which correspond to the weighted value of key words, I identified the major key words for the occurrence of the civil complaint for the officially assessed land price. Then the spatio-temporal characteristics of the civil complaints were examined by analysing hot spot based on the statistics of Getis-Ord $Gi^*$. It was found that the characteristic of civil complaints for the officially assessed land price were changing, forming a cluster that is linked spatio-temporally. Using text mining and social network analysis method, we could find out that the occurrence reason of civil complaints for the officially assessed land price could be identified quantitatively based on natural language. TF and TF-IDF, the weighted averages of key words, can be used as main explanatory variables to analyze spatio-temporal characteristics of civil complaints for the officially assessed land price since these statistics are different over time across different regions.

• Therapeutic Science for Rehabilitation
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• v.10 no.1
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• pp.7-17
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• 2021

Objective : The purpose of this study was to quantitatively analyze the quantitative changes in, and the status of, overseas occupational therapy using text mining. Methods : Using PubMed, research papers on Elderly, Health and Occupational therapy published between 2009 and 2019 were selected for analysis, Abstracts of the selected papers were analyzed. The number of annual papers, the key words, the key words by year, and the relationship between the words were analyzed. Results : The number of papers published from 2009 to 2019 was 9,941, there was a gradual increase from 2009 to the highest in 2017 or 2018, followed by a decreasing trend in 2019. Within the last five years, the most frequent words were Care, Group, Intervention, Pain, Treatment, and Work. There was a strong relationship between the words based on the average frequency over the last 11 years, function, health, event, and partition. Conclusion : This study is meaningful because it applied a new research method called text mining to the empirical and systematic analysis of trends in occupational therapy and presented macroscopic and comprehensive results. The findings are expected to help establish new research directions at clinical and research sites for occupational therapy related to older adults.

• Korean Association of Arts Management
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• no.58
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• pp.153-177
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• 2021

This study attempted to analyze language networks based on the theory of art policy in the New Normal era triggered by COVID-19 and domestic and foreign policy trends. For analysis, data containing key words of "Corona" and "Art" were collected from Google News and Web documents from March to September 2020 to extract 227 refined subject words, and the extracted subject words were analyzed as indicators of frequency and centrality of subject words through the Netminor program. In addition, visualization analysis of semantic networks has been attempted for the analysis of relationships between each topic languages. As a result of the semantic network analysis, the most frequent topic was "Corona," and "Culture and Art," "Art," "Performance," "Online" and "Support" were included in the group with the most frequencies. In the centrality analysis, "Corona" was the most popular, followed by "the era," "after," "post," "art," and "cultural arts," with high frequency, "Corona," "art," and "cultural arts" also dominated most centrality. In particular, the top-level key words in the analysis of frequency and centrality of the topic are 'online' and 'support' and 'policy'. This can be seen as indicating that the rapid rise of non-face-to-face and online content and support policies for the artistic communities are needed due to the dailyization of social distance due to COVID-19.

• Journal of Navigation and Port Research
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• v.32 no.8
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• pp.685-692
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• 2008

The goal of this paper is to set the research and development (R&D) strategic plan of Honam Sea Grant (HSG) program which is to secure the base technologies for the success of Jeollanamdo's policy projects in the area of maritime and fisheries. HSG's mission is to support science-based sustainable management, conservation and enhancement of Honam coastal and aquatic resources through research, extension and education. Firstly, 80 cases of Jeollanam-do's policy project and 48 cases of HSG's R&D project are compiled and classified into the five areas of maritime and fisheries. Secondly, typical key words are extracted from each five areas and assessed the inherent meanings of each key words using quarterly segmented meaning allocation techniques with 'intended for practical use', 'intended for theoretical use', 'intended for future', and 'intended for current'. Then, we proposed R&D strategic plan based on the evaluation results and, it's practical use is also discussed.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.261-270
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• 2020

In this study, we identify promising technologies for Piggyback transportation system by analyzing the relevant patent information. In order for this, we first develop the patent database by extracting relevant technology keywords from the pioneering research papers for the Piggyback flactcar system. We then employed textmining to identify the frequently referred words from the patent database, and using these words, we applied the LDA (Latent Dirichlet Allocation) algorithm in order to identify "topics" that are corresponding to "key" technologies for the Piggyback system. Finally, we employ the ARIMA model to forecast the trends of these "key" technologies for technology forecasting, and identify the promising technologies for the Piggyback system. with keyword search method the patent analysis. The results show that data-driven integrated management system, operation planning system and special cargo (especially fluid and gas) handling/storage technologies are identified to be the "key" promising technolgies for the future of the Piggyback system, and data reception/analysis techniques must be developed in order to improve the system performance. The proposed procedure and analysis method provides useful insights to develop the R&D strategy and the technology roadmap for the Piggyback system.

• Economic and Environmental Geology
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• v.55 no.3
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• pp.273-280
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• 2022

The purpose of this study is to explore the social perception on geoscience using a big data analysis and to propose a way to enhance people's perception on geoscience. For this, 5,044 media articles including geoscience produced by 54 media companies from January 1, 2010 to April 14, 2022. were analyzed. Big data analyses were applied. The results of analyses are as follows: Media articles consist of key words of research institute, some countries of America, China and Japan, City of Pohang, CEO of KIGAM. And geology, industry, development of mineral resources, environment, energy, nuclear power, and groundwater are highlighted as key words. Also, it is confirmed that topics related to geoscience such as expert, environment and research institute are not individually isolated, but interconnected and linked to topics in the center of future, industry, and global. Based on this result, ways to enhance people's perception on geoscience were discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2768-2787
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• 2023

As an important research direction of the application of computer science in the medical field, the automatic generation technology of radiology report has attracted wide attention in the academic community. Because the proportion of normal regions in radiology images is much larger than that of abnormal regions, words describing diseases are often masked by other words, resulting in significant feature loss during the calculation process, which affects the quality of generated reports. In addition, the huge difference between visual features and semantic features causes traditional multi-modal fusion method to fail to generate long narrative structures consisting of multiple sentences, which are required for medical reports. To address these challenges, we propose a multi-feature optimization Transformer (MFOT) for generating radiology reports. In detail, a multi-dimensional mapping attention (MDMA) module is designed to encode the visual grid features from different dimensions to reduce the loss of primary features in the encoding process; a feature pre-fusion (FP) module is constructed to enhance the interaction ability between multi-modal features, so as to generate a reasonably structured radiology report; a detail enhanced attention (DEA) module is proposed to enhance the extraction and utilization of key features and reduce the loss of key features. In conclusion, we evaluate the performance of our proposed model against prevailing mainstream models by utilizing widely-recognized radiology report datasets, namely IU X-Ray and MIMIC-CXR. The experimental outcomes demonstrate that our model achieves SOTA performance on both datasets, compared with the base model, the average improvement of six key indicators is 19.9% and 18.0% respectively. These findings substantiate the efficacy of our model in the domain of automated radiology report generation.