• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.440-448
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• 2019

In this paper, we propose an ordering chatbot that can process multiple keywords based on recursive slot-filling method. In general, in case of an order service using chatbots, the whole order process is performed only according to the sequence defined by the developer. That is, among all the information needed for the whole order process, only one input can be processed at one time. In order to reduce processing step for the order, we propose a recursive slot-filling method which fills out multiple slots per one time by extracting multiple keywords. First, a keyword array for the order is created according to the order related information. Next, from the input sentence of a user, multiple keywords is extracted. Corresponding slots for a keyword array will be filled with the extracted keywords. Finally, recursive routine will be executed to fill out all the blank in the keyword array. The usability and validity of the proposed method will be shown from the implementation of a smartphone application.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.147-178
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• 2021

In this study, co-word analysis was conducted to investigate the intellectual structure of the domestic open access area. Through KCI and RISS, 124 research articles related to open access in Korea were selected for analysis, and a total of 1,157 keywords were extracted from the title and abstract. Network analysis was performed on the selected keywords. As a result, 3 domains and 20 clusters were extracted, and intellectual relations among keywords from open access area were visualized through PFnet. The centrality analysis of weighted networks was used to identify the core keywords in this area. Finally, 5 clusters from cluster analysis were displayed on a multidimensional scaling map, and the intellectual structure was proposed based on the correlation between keywords. The results of this study can visually identify and can be used as basic data for predicting the future direction of open access research in Korea.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.319-325
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• 2021

The urban regeneration project is an activity that promotes the economy in underdeveloped commercial or residential areas and maintains urban communities through improvement of living in residential areas. Through this study, various successful cases of urban regeneration at home and abroad and surveys related to urban regeneration in Bongsan Village, Yeongdo, Busan were collected and analyzed. Key keywords for each case were derived, grouped, and top keywords were created. The 13 top keywords were evaluated using Likert's 5-point scale, and AHP was conducted for the 10 keywords that were finally selected. As a result of AHP analysis, the preference for "spatial and physical properties" was derived in the order of "publicity", "sustainability", and "identity". The preference of "content and system properties" was derived in the order of "resident participation", "convenience", "locality", and "local government participation". It is necessary to present a role as a design guideline for establishing urban regeneration designs in relation to various urban regeneration projects that will become more active in the future through the analysis of preferences of urban regeneration keywords derived through this study.

• Journal of the Korean Institute of Rural Architecture
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• v.24 no.3
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• pp.47-57
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• 2022

This study attempted to grasp the status and perception of Uljin Geumgangsong by grasping mass media issues and user perception using big data, and to present basic data when constructing monitoring using user perception by examining the establishment relationship of agenda setting from a time-series perspective. The results of collecting and analyzing text data that can identify mass media and visitor awareness are as follows. First, both mass media and visitor keywords were related to the importance of the value and meaning of Uljin Geumgangsong. Second, in the case of the connection network, Geumgang Pine Agriculture was centered, but in the case of difference in perception between mass media and visitors, such results were derived due to the object of interest. Third, in the case of the connection relationship structure, the connection strength was strong because there were many overlapping contents of mass media. Fourth, as a result of the centrality analysis, both mass media and visitor-aware keywords were positively recognized as spaces created and maintained through institutional support, and objective perception could be grasped by finding hidden keywords. Fifth, as a result of time series analysis, it was possible to grasp the flow through the issue keywords that appeared by period, and unlike the past, it was recognized as a place for tourism and travel. Finally, as a result of examining whether the agenda setting is consistent, there is a mass media influence, so it is thought that more diverse and more information and publicity are needed by utilizing it.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.197-210
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• 2024

Purpose : This study aimed to analyze research trends in the field of integrative medicine through a bibliometric analysis of articles published in Integrative Medicine Research (IMR) journal from 2017 to 2022. Methods : Articles published in IMR journal between 2017 and 2022 were searched using the Web of Science database on August 22, 2023. The analysis was performed using the Bibliometrix and Biblioshiny tools in R (version 4.3.1) and VOSviewer (version 1.6.19). Results : The key findings were as follows: average citations per article (9.41), total authors (1,142), single-authored articles (12), average articles per author (0.27), average co-authors per article (5.27), and rate of international co-authorships (15.69 %). The most-cited article was on the cryopreservation of cells or tissues and their clinical applications. The top keyword analysis by author keywords showed that "acupuncture" was the most frequently used keyword (33 times). Co-occurrence network analysis showed 85 high-frequency keywords that appeared five or more times, and the top five keywords by total link strength were "acupuncture," "herbal medicine," "prevalence," "alternative medicine," and "complementary." The study found that, contrary to the trend in complementary and alternative medicine research in Korea, the IMR journal actively conducts intervention studies to provide clinical evidence. Conclusion : In the IMR journal, "acupuncture" was the most frequent of author keywords. The analysis of keyword trend topics over time showed that the keyword "systematic review" continued to appear from 2020 to 2022, and the keyword "clinical practice guideline" appeared for the first time in 2021. In particular, the co-occurrence network analysis highlighted keywords related to intervention research, in contrast to domestic research trends. While this study analyzed only one journal, future studies expanding the category of integrative medicine and increasing the number of journals analyzed may provide further insights.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.179-184
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• 2024

Franchise are now a red ocean in Food industry and they need to find other options to appeal for their product, the uprising content, food tech. The franchises are working on R&D to help franchisees with the operations. Through this paper, we analyze the franchise interest on food tech and to help find the necessity of development for franchisees who are in needs with hand, not of human, but of technology. Using Textom, a big data analysis tool, "franchise" and "food tech" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 01 January, 2023 to 31 December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "food tech" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, a total of 10,049 words were derived, and among them, the top 50 keywords with the highest relevance and search frequency were selected and applied to this study. The top 50 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. By using big data analysis, it was found out that franchise do have interest on food tech. "technology", "franchise", "robots" showed many interests and keyword "R&D" showed that franchise are keen on developing food tech to seize competitiveness in Franchise Industry.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.115-124
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• 2022

The purpose of this study is to analyze and present keywords, trends, and language networks of keywords for each year of the pedagogical exam of the secondary teacher's employment exam for the 2019~2022 school year. The main research methods were text mining technique and language network analysis method, and analysis programs were KrKwic, Wordcloud Maker, Ucinet6, NetDraw, etc. The research results are as follows; First, keywords such as teacher, student, curriculum, class, and evaluation appeared in the top rankings, and keywords (online, wiki, discussion ceremony, information, etc.) that reflect the recent online class progress in the current COVID-19 situation also tended to appear. The keywords with high frequency of occurrence in the four-year integrated text were student(44), teacher(39), class(27), school(18), curriculum(16), online(10), and discussion method(8). Second, the overall language network of the keywords with high frequency of 4 years showed a significant level of density(0.566), total number of links(492), and average degree of links(16.4). The degree centrality was found in the order of teacher(199.0), class(197.0), student(185.0), and school(150.0). Betweenness centrality was found in the order of teacher(30.859), class(18.956), student(16.054), and school (15.745). It is expected that the results of this study will serve as data to be considered for preparatory teachers, institutions and related persons, and teachers and administrators of secondary school teacher training institutions.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.457-473
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• 2023

Asbestos has been produced, imported and used in various industries in Korea over the past decades. Since asbestos causes fatal diseases such as malignant mesothelioma and lung cancer, the use of asbestos has been generally banned in Korea since 2009. However, there are still many asbestos-containing materials around us, and safe management is urgently needed. This study aims to examine asbestos-related trend changes using major asbestos-related keywords based on the asbestos trend analysis using big data for the past 32 years (1991 to 2022) in Korea. In addition, we reviewed both domestic trends related to the production, import, and use of asbestos before 1990 and asbestos-related policies from 2023 to 2027. From 1991 to 2000, main keywords related to asbestos were research, workers, carcinogens, and the environment because the carcinogenicity of asbestos was highlighted due to domestic production, import, and use of asbestos. From 2001 to 2010, the main keywords related to asbestos were lung cancer, litigation, carcinogens, exposure, and companies because lawsuits were initiated in the US and Japan in relation to carcinogenicity due to asbestos. From 2011 to 2020, the high ranking keywords related to asbestos were carcinogen, baseball field, school, slate, building, and abandoned asbestos mine due to the seriousness of the asbestos problem in Korea. From 2021 to present (2023), the main search keywords related to asbestos such as school, slate (asbestos cement), buildings, landscape stone, environmental impact assessment, apartment, and cement appeared.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Journal of Korean Society of Rural Planning
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• v.22 no.2
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• pp.45-55
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• 2016

From 1970, the concept of ecotourism is introduced, lots of studies in ecotourism appeared. Review these studies are necessary for future ecotourism studies. Some review studies on ecotourism are existed. However, these approach also limitation of subjectivities and some sorts of papers has not been reviewed. This study use keyword network analysis which is used as big data analysis to overcome the limitation. Foreign 2455 studies and domestic 163 studies which have ecotoursim in keywords, are analyzed for reviewing. As a result, 3 cluster('Sustainable tourism development', 'Ecological conservation', 'Ecotourist analysis' appeared, in ecotourism studies. In addition, this cluster has deep relationship with region. 'Sustainable tourism development' is related to Eurasia, Australia, Europe. 'Ecological conservation' is related to Africa. 'Ecotourism analysis' is related to North America. Especially 'Resident participation', 'Stakeholder' are appeared many times in Asia region. These results show that ecotourism studies are interpreted in regional contexts. It means that although only one word 'ecotourism' is used in different contexts, regional approach are needed for exact use. In Korea, the keywords are focused on ecotourists and developments. As Korea has lots of ecotour village, resident participation studies have to be supplemented.