• The Korea Journal of Herbology
• /
• v.38 no.6
• /
• pp.61-71
• /
• 2023

Objectives : This study aimed to find out the future direction of accreditation system of Extramural herbal dispensaries (EHD) by comparing the current criteria of EHD and the existing Korea good manufacturing practice (GMP) regulations. Methods : Among the accreditation criteria of EHD, criteria of general herbal medicine was compared with the pharmaceutical GMP of Korea. The regulations of the accreditation of EHD and the regulations of KGMP were compared and organized with similar things based on the index of KGMP. All criteria from both were extracted for each element, classified into key-words and evaluated by dividing them into the same, similar one and no-matching. Results : Among the 189 criteria of KGMP, 77 criteria were consistent with the accreditation of EHD, and 15 criteria were similar. Based on the accreditation of EHD, 70.4% of the criteria were consistent or similar to KGMP. There were a total of 27 key-words only in the GMP criteria and not in the EHD one. Hence, a total of 25 key-words only in the EHD criteria and not in the GMP one. There were 12 similar key-words, and among them, there were 4 key-words in which accreditation of EHD was more specific than the KGMP. Conclusions : The criteria of general herbal medicine in EHD showed a similar or equivalent level of accreditation criteria compared to that of pharmaceutical GMP in Korea, and it ts believed that it should be considered at the current level to reflect the characteristics of herbal medicine.

• Journal of health informatics and statistics
• /
• v.43 no.4
• /
• pp.274-281
• /
• 2018

Objectives: The purpose of this study is to comprehensively compare the trends of health administration and health policy in the field of health care using the semantic network analysis in the inaugural address of the Ministry of Health and Welfare of each regime in Korea. Methods: This study used a language network analysis method that uses Korean Key Words In Context (KrKwic) program and NetMiner program in sequence. The analysis was conducted by Minister Hwa-joong Kim during the Moo-hyun Roh government, Minister Jae-hee Jeon during the Myung-bak Lee government, Minister Young Jin of Geun-hye Park government and Government Jae-in Moon's inaugural address of Neung-Hoo Park Minister, respectively. Results: The key words differentiated by each regime are that the Moo-hyun Roh Government's Minister Hwa-joong Kim had high connection centrality values in the words 'balanced development', 'comprehensive' and 'reform'. Minister Jae-Hee Jeon of Myung-bak Lee Government had high connection centrality values in the words 'poverty' and 'return'. In the case of Minister Young Jin of Geun-hye Park Government had high connection centrality values in the words 'demand', 'Customized' and 'Life cycle'. In the case of Minister Neung-Hoo Park of Jae In Moon Government had high connection centrality values in the words 'Welfare state', 'Embracing' and 'Soundness'. Conclusions: If the role of health administration in the health care field and the health care policies are constantly changed according to the policies of each regime, it is inconsistent and it is difficult to approach from the long term perspective for public health promotion. In the future, health policy should be developed and implemented with a long-term perspective and consistency based on the consensus and participation of the people with less influence on the change and direction of each government's policies.

• Journal of KIISE
• /
• v.44 no.11
• /
• pp.1236-1243
• /
• 2017

In this paper, we propose a novel way of producing keyword networks, named LSI-based ClusterTextRank, which extracts significant key words from a set of clusters with a mutual information metric, and constructs an association network using latent semantic indexing (LSI). The proposed method reduces the dimension of documents through LSI, decomposes documents into multiple clusters through k-means clustering, and expresses the words within each cluster as a maximal spanning tree graph. The significant key words are identified by evaluating their mutual information within clusters. Then, the method calculates the similarities between the extracted key words using the term-concept matrix, and the results are represented as a keyword association network. To evaluate the performance of the proposed method, we used travel-related blog data and showed that the proposed method outperforms the existing TextRank algorithm by about 14% in terms of accuracy.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.36 no.1
• /
• pp.27-35
• /
• 2012

This study locates typical Korean fashion images in domestic and foreign fashion journals to advance Korea's international image in contemporary global fashion markets. The investigation of the frequency of articles and their types (so as to inquire into interest in Korean fashion in the global fashion markets) showed that for the appearance frequency of domestic articles studied, a good number of articles were published in the first half of 2008 and in 2009. In the case of foreign articles, the number of them increased from the second half of 2008 and the majority of articles were shown in the first half of 2010. Second, the investigation of the appearance features by article type studied in order to understand how Korean fashion played a role in the world's markets. The majority of articles were related to fashion brands that entered Chinese market in fashion brand articles in the case of domestic articles; however, many foreign articles introduced designers that participated in global fashion collections in Paris and New York. Third, as a result of analyzing typical key words by article type in order to find key words which could enhance Korea's fashion national image representing, we could confirm that 'Korean designers' can be a typical key words to represent Korean fashion. The key word most exposed in both domestic and foreign articles was 'designer Lie Sang Bong' and only his articles contained the content about influential Korean design materials.

• Korean Journal of Social Welfare
• /
• v.68 no.2
• /
• pp.185-211
• /
• 2016

This study analyzes key word networks of the papers which are published at Korean Journal of Social Welfare issued by Korean Academy of Social Welfare from 1979 to 2015. It aims at investigating the trends of social welfare researches in Korea by dividing the given period into two: 1979-2000 and 2001-2015. It shows the trends in three ways: methodologies, subjects, and intellectual structures. In order to identify intellectual structure, it calculate centrality indices basing on co-appearance frequency of key words. It also derives some values which explain relationship structure of key words by using pathfinder algorithm, and finally visualizes the intellectual structures by using the NodeXL program. Some implications of the findings of these analyses are discussed in the end.

• Journal of Digital Convergence
• /
• v.14 no.12
• /
• pp.173-182
• /
• 2016

AI (Artificial Intelligence) has attracted interest as a key element for technological advancement in various fields. In Korea, internet companies are leading the development of AI business technology. Active government funding plans for AI technology has also drawn interest. But not everyone is optimistic about AI. Both positive and negative opinions coexist about AI. However, attempts on analyzing people's opinions about AI in a quantitative way was scarce. In this study, we used text mining on SNS (Social Networking Service) to collect opinions about AI. And then we performed a comparative analysis about whether people view it as a positive thing or a negative thing and performed a comparative analysis to recognize popular key-words. Based on the results, it was confirmed that the change of key-words and negative posts have increased through time. And through these results, we were able to predict trend about AI.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.13 no.6
• /
• pp.137-145
• /
• 2013

Recently most mobile devices have soft keyboards on their LCD touch screens. Because of the tiny size of the touch screen of the soft keyboard, adjacent keys are mistakenly typed. Also utilizing a key for multiple key inputs causes key type errors. In this paper, we proposed an algorithm to recommend proper words to the user while the user continues to type keys, which helps to easily correct key type errors. In addition, we presented a soft keyboard called MissLess which implemented the recommendation algorithm. We evaluated recommendation performance of MissLess keyboard through experiments by using 3 test sets. The test results showed that the success ratio of recommendation reached up to about 90% although there were some differences between results. However it is needed to be considered that we recommended 4 words for an input word in this experiments.

• Journal of Information Processing Systems
• /
• v.9 no.4
• /
• pp.602-620
• /
• 2013

Many previous research studies on extractive text summarization consider a subset of words in a document as keywords and use a sentence ranking function that ranks sentences based on their similarities with the list of extracted keywords. But the use of key concepts in automatic text summarization task has received less attention in literature on summarization. The proposed work uses key concepts identified from a document for creating a summary of the document. We view single-word or multi-word keyphrases of a document as the important concepts that a document elaborates on. Our work is based on the hypothesis that an extract is an elaboration of the important concepts to some permissible extent and it is controlled by the given summary length restriction. In other words, our method of text summarization chooses a subset of sentences from a document that maximizes the important concepts in the final summary. To allow diverse information in the summary, for each important concept, we select one sentence that is the best possible elaboration of the concept. Accordingly, the most important concept will contribute first to the summary, then to the second best concept, and so on. To prove the effectiveness of our proposed summarization method, we have compared it to some state-of-the art summarization systems and the results show that the proposed method outperforms the existing systems to which it is compared.

• Journal of Internet Computing and Services
• /
• v.16 no.3
• /
• pp.43-57
• /
• 2015

In this thesis was conducted to propose a method for efficient information retrieval using concept lattices. Since this thesis designed a new system based on ordinary concept lattices, it has the same approach method as ontology, but this thesis proposes new concept lattices to be used by establishing collaborative relations between objects and concepts that users are likely to search information more efficiently. The system suggested by this thesis can be summarized as below. Firstly, this system leads to a collaborative search by using Three kinds of concepts, such as keyword concept lattices, which focus on input key words, expert concept lattices recommended by experts and theme concept lattices, and based on these 3 concept lattices, it will help users search information they want more efficiently. Besides, as the expert concept and the keyword concept become combined, further providing users with the frequency of keyword and the frequency of category, this system can function to recommend key words related to search words entered by users. Another function of this system is to inform users of key words and categories used in users' interested themes by using the theme concept lattices. Secondly, when there is not keyword entered by a user, it is possible for users to achieve the goal of search through the secondary search when this system provides them with key words related to the input keyword. Thirdly, since most of the information is managed while being dispersed, such dispersed and managed information not only has different expression methods but changes as time goes. Accordingly, By using XMDR for efficient data access and integration of distributed information, this thesis proposes a new technique and retrieval system to integrate dispersed data.

• Journal of Korean Home Economics Education Association
• /
• v.30 no.4
• /
• pp.93-112
• /
• 2018

We analysed the textbooks of Home Economics in middle school from 1^st to 2009 curriculums to investigate the contents and the portion of Food and Nutrition section. The key words were generated by word cloud technique using text-mining, and the portion of Food and Nutrition section was presented as a ratio of the pages. The core key words of Food and Nutrition section through the curriculums were 'raw food'·'food'·'diet'. In 1^st and 2^nd curriculums, the main key words were related to food materials, condiments and nutrients such as 'vitamin'·'protein'. The words such as 'nutrition'·'eating'·'requirement' were newly appeared in 3^rd, 'portion' in 6^th, and 'diet'·'adolescence' in 7^th curriculum. The mean ratio of Food and Nutrition section in Home Economics was 24.3%. While the portion was as high as 31.8% in 7th it was strikingly reduced to 15.2% in 2009^th. curriculum. Besides, Food and Nutrition section was composed of 10 units of middle level category during the 2^nd and 3^rd curriculums, and was reduced to 2 small units with none of middle level category in 2009^th curriculum. Although the contents of Food and Nutrition section has been developed and adapted to the needs of the society through the curriculums, the portion of Food and Nutrition section in Home Economics has been reduced especially in 2009^th curriculum, which could raise concerns on the health of individuals and communities.