• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.113-118
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• 2010

The single-sourcing of content is extremely beneficial because when we are managing several projects with hundreds or thousands of documentation, we don't want to be changing the same content, or substantially similar content in multiple locations. The Darwin Information Typing Architecture (DITA) is an XML-based architecture for authoring, producing, and delivering technical documents. It consists of a set of design principles for creating Information -typed topic modules and for using that content in various ways. In this paper, we examine the approach of using The Darwin Information Typing Architecture for technical documents development to enhance the reuse of existing content components for difference information products.

• Journal of Information Technology Applications and Management
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• v.30 no.1
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• pp.53-70
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• 2023

Artificial intelligence has become familiar with modern society, not the distant future. As artificial intelligence and machine learning developed more highly and became more complicated, it became difficult for people to grasp its structure and the basis for decision-making. It is because machine learning only shows results, not the whole processes. As artificial intelligence developed and became more common, people wanted the explanation which could provide them the trust on artificial intelligence. This study recognized the necessity and importance of explainable artificial intelligence, XAI, and examined the trends of XAI research by analyzing social networks and analyzing topics with IEEE published from 2004, when the concept of artificial intelligence was defined, to 2022. Through social network analysis, the overall pattern of nodes can be found in a large number of documents and the connection between keywords shows the meaning of the relationship structure, and topic modeling can identify more objective topics by extracting keywords from unstructured data and setting topics. Both analysis methods are suitable for trend analysis. As a result of the analysis, it was found that XAI's application is gradually expanding in various fields as well as machine learning and deep learning.

• The Journal of Internal Korean Medicine
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• v.43 no.6
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• pp.1075-1088
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• 2022

Objectives: The aim of this study is to analyze cancer research trends in traditional Korean medical journals indexed in the Korea Citation Index since 2000. Methods: Cancer research papers published in traditional Korean medical journals were searched in databases from inception to October 2022. The numbers of publications by journal and by year were descriptively assessed. After natural language processing, topic modeling (based on Latent Dirichlet allocation) and keyword network analysis were conducted. Results: This research trend analysis involved 1,265 papers. Six topics were identified by topic modeling: case reports on symptom management, literature reviews, experiments on apoptosis, herbal extract treatments of breast carcinoma cell lines, anti-proliferative effects of herbal extracts, and anti-tumor effects. Keyword network analysis found that the effects of herbal medicine were assessed in clinical and experimental studies, while acupuncture was mainly mentioned in clinical reports. Conclusions: Cancer research papers in traditional Korean medical journals have contributed to evidence-based medicine. Further experimental studies are needed to elucidate the effects of on different hallmarks of cancer. Rigorous clinical studies are needed to support clinical guidelines.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.1
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• pp.53-70
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• 2022

Recently, due to the change to SASB(Sustainability Accounting Standards Board) and GRI(Global Reporting Initiative) Standards 2021, the paradigm for non-financial information disclosure is changing significantly, with the number of ESG topics and indicators that must be disclosed by industry from an autonomous material topic selection method. This study revealed that the number of compulsory topics in the oil and gas industry by GRI standards 2021 is up to 2.4 times higher than the average number of material topics disclosed when domestic companies publish sustainability reports using GRI Standards 2020. In the oil and gas industry, I analyzed the similarities and differences between the GRI standards 2021 and the ESG topics covered by SASB by environmental, social, economic, and governance areas. In addition, the materiality test process, which is different in GRI standards 2021, is introduced, and the issues included in the following 10 representative ESG-related initiatives are summarized into 62 and suggested improvement plans for materiality test used in the topic pool.

• Korea Trade Review
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• v.47 no.6
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• pp.189-209
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• 2022

This study aims to analyze Latin America and the Caribbean papers published in Korea during the past 2000-2020 years. Through this study, it is possible to understand the main subject and direction of research in Korea's Latin America and the Caribbean area. As the research mythologies, this study uses the text mining and Social Network Analysis such as frequency analysis, several centrality analyses, and topic analysis. After analyzing the empirical results, there has been a tendency to change the key words and centrality coefficients between 2000-2010 and 2011-2020 years. During 2011-2020 years, the most frequent keywords were changed from Neoliberalism and culture to policy education, and economy related words. The degree and closeness centrality analyses appeared the higher frequency key words. However, the eigenvector centrality appeared very different from the order of frequency key words. The topic analysis shows that the culture, language, and Neoliberalism were the most important keywords during 2000-2010 years but economy, labor trade, industry, development became the most important keywords during 2011-2020 years in topics.

• Korea Trade Review
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• v.45 no.3
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• pp.55-69
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• 2020

This study examines the research trends and knowledge structure of international trade studies using topic modeling method, which is one of the main methodologies of text mining. We collected and analyzed English abstracts of 1,868 papers of three Korean major journals in the area of international trade from 2003 to 2019. We used the Latent Dirichlet Allocation(LDA), an unsupervised machine learning algorithm to extract the latent topics from the large quantity of research abstracts. 20 topics are identified without any prior human judgement. The topics reveal topographical maps of research in international trade and are representative and meaningful in the sense that most of them correspond to previously established sub-topics in trade studies. Then we conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. We discovered 2 hot topics(internationalization capacity and performance of export companies, economic effect of trade) and 2 cold topics(exchange rate and current account, trade finance). Trade studies are characterized as a interdisciplinary study of three agendas(i.e. international economy, International Business, trade practice), and 20 topics identified can be grouped into these 3 agendas. From the estimated results of the study, we find that the Korean government's active pursuit of FTA and consequent necessity of capacity building in Korean export firms lie behind the popularity of topic selection by the Korean researchers in the area of int'l trade.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.343-356
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• 2024

Super app is an application that provides a variety of services in a unified interface within a single platform. With the acceleration of digital transformation, super apps are becoming more prevalent. This study aims to suggest service enhancement measures by analyzing the user review data before and after the transition to a super app. To this end, user review data from a payment-based super app(Shinhan Play) were collected and studied via topic modeling. Moreover, a matrix for assessing the importance and usefulness of topics is introduced, which relies on the eigenvector centrality of the inter-topic network obtained through topic modeling and the number of review recommendations. This allowed us to identify and categorize topics with high utility and impact. Prior to the transition, the factors contributing to user satisfaction included 'payment service,' 'additional service,' and 'improvement.' Following the transition, user satisfaction was associated with 'payment service' and 'integrated UX.' Conversely, dissatisfaction factors before the transition encompassed issues related to 'signup/installation,' 'payment error/response,' 'security authentication,' and 'security error.' Following the transition, user dissatisfaction arose from concerns regarding 'update/error response' and 'UX/UI.' The research results are expected to be used as a basis for establishing strategies to strengthen service competitiveness by making super app services more user-oriented.

• Journal of the Korea Fashion and Costume Design Association
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• v.26 no.2
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• pp.87-99
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• 2024

Freediving, an underwater leisure sport that involves diving without the use of a breathing apparatus, has gained popularity among younger demographics through the viral spread of images and videos on social media platforms. This study employs prominent Big Data analysis techniques, including text mining, Latent Dirichlet Allocation (LDA) topic analysis, and opinion mining to explore the keywords associated with freediving suits over the past five years. The research aims to analyze the rapidly evolving market trends of freediving suits and the increasingly complex and diverse consumer perceptions to provide foundational data for activating the freediving suit market and developing strategies for sustained growth. The study identified the keyword 'size' related to freediving suits and conducted opinion mining on 'freediving suit sizes'. Although the results showed a higher positive than negative sentiment, negative keywords were also extracted, indicating the need to understand and mitigate the negative factors associated with 'size'. The findings offer vital guidelines for the advancement of the freediving suit market and enhancing consumer satisfaction. This study is important as it contributes foundational data for continuous growth strategies of the freediving suit market.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.63-71
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• 2019

Recently, there has been a lot of research on the construction of smart factory in the 4th Industrial Revolution era. Among the various technologies involved in the deployment of smart factory, one of the key technologies is the IoT protocol sector that handles the transmission and reception of data. In this regard, the MQTT protocol is generally used most commonly, but the existing MQTT technology lacks the concept of priorities of messages, so it is somewhat insufficient to be applied to an industrial field requiring real-time property. Priority handling of urgent messages is critical, especially in emergency situations, such as the emergency shutdown of the entire relevant facility following the failure of a particular facility. To improve this, research on priority-based MQTT is being conducted somewhat, but these studies have problems with actual field use because they are a variant of the MQTT standard. Therefore, this study conducts and verifies studies related to MQTT, which can prioritize messages while adhering to existing MQTT standards.

• Journal of Digital Convergence
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• v.14 no.3
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• pp.251-260
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• 2016

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patent, and project reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer the further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a RDB-to-Triples transformer is implemented. Lastly, we show an experiment on R&D data integration using the lightweight ontology, triples generation, and visualization and navigation of the knowledge map.