• Journal of Physiology & Pathology in Korean Medicine
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• v.28 no.4
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• pp.365-370
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• 2014

The intrinsic concepts of the three yin and three yang diseases in is unclear yet in spite of considerable controversy. In order to answer these problems, the structures of pathological transmission and anatomical terms used in the text were analyzed first. On these structural bases, the theoretical background and differential therapeutic principles of three yin and three yang disease classification. The organic structures frequently used in the text were heart, stomach, pancreas, blood chamber and urinary bladder, and the important regions in the transmission were chest, flank, epigastrium, abdomen, hypogastrium, groin on the other hand. When a host is invaded by extrinsic pathogen, an affinity is formed between the two based on the similarity of epidermal density condition and nutrient-defense features and existing disorders in the body. And then the symptoms show in 3 stages with 6 patterns in the general infective diseases. The initial stage is the period that the syndrome is limited in the external flesh area, and it mainly corresponds with taiyang bing besides the other exterior patterns of 3 yang and 2 yin bing. The middle stage is to the climax after the end of initial stage and it corresponds with mainly yangming bing including shaoyang and taiyin bing. In the terminal stage, the host gradually falls into exhaustive step or recovery phase, corresponding with shaoyin and jueyin bing. Conclusively, these dual meanings of three yang and yin should be a first guide and principle of treatment against various infective diseases.

• The Journal of Industrial Distribution & Business
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• v.11 no.5
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• pp.45-52
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• 2020

Purpose: In the 4th industrial revolution, services that incorporate various smart technologies in the tourism sector have begun to gain popularity. Accordingly, academic discussions on smart tourism have also started to become active in various fields. Despite recent research, the definition of smart tourism is still ambiguous, and it is not easy to differentiate its scope or characteristics from traditional tourism concepts. Thus, this study aims to analyze the perception of smart tourism exposed online to identify the current point of smart tourism in Korea and present the research direction for conceptualizing smart tourism suitable for the domestic situation. Research design, data, and methodology: This study analyzes the perception of smart tourism exposed online based on 20,198 news data from portal sites over the past six years. Data on words used with smart tourism were collected from the leading portal sites Naver, Daum, and Google. Text mining techniques were applied to identify the social awareness status of smart tourism. Network analysis was used to visualize the results between words related to smart tourism, and CONCOR analysis was conducted to derive clusters formed by words having similarity. Results: As a result of keyword analysis, the frequency of words related to the development and construction of smart tourism areas was high. The analysis of the centrality of the connection between words showed that the frequency of keywords was similar, and that the words "smartphones" and "China" had relatively high connection centrality. The results of network analysis and CONCOR indicated that words were formed into eight groups including related technologies, promotion, globalization, service introduction, innovation, regional society, activation, and utilization guide. The overall results of data analysis showed that the development of smart tourism cities was a noticeable issue. Conclusions: This study is meaningful in that it clearly reflects the differences in the perception of smart tourism between online and research trends despite various efforts to develop smart tourism in Korea. In addition, this study highlights the need to understand smart tourism concepts and enhance academic discussions. It is expected that such academic discussions will contribute to improving the competitiveness of smart tourism research in Korea.

• Journal of Korean Medical classics
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• v.31 no.2
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• pp.71-82
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• 2018

Objectives : The purpose of this study is to compare the similarities among Donguibogam(DO), Uihagibmun(UI), and Gyeongagjeonseo(GY) in order to examine whether the medical thoughts embedded in the texts can be compared in a quantitative way. Methods : Under an empirical assumption that medical thoughts can be reduced to the frequency of major key words within the text, we selected the fourteen words of the four categories that are commonly used to describe physiology and pathology in Korean medicine as key words. And the frequency of these key words was measured and compared with each other in the three important medical texts in Korea. Results : As a result of quantitative analysis based on ${\chi}^2$ statistic, the key words in the books were distributed most heterogeneously in DO and distributed most homogeneously in UI. In comparison of the similarity analyzed by the same method, DO and UI were significantly more similar than those of DO and UI. The results of the word frequency pattern and the similarities of the book contents(CBDF) show that DO is influenced by UI, and the differences between standardized residuals and homogeneity tells us that internal context of both books are constructed differently. Conclusions : These results support the results of traditional research by experts. With the above, we were able to confirm that medical thoughts can be reduced to the frequency of major key words within the text, and compared through the frequency of such key words.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.61-77
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• 2017

Recently, the number of academic conference information on the Internet has rapidly increased, the automatic classification of academic conference information according to research subjects enables researchers to find the related academic conference efficiently. Information provided by most conference listing services is limited to title, date, location, and website URL. However, among these features, the only feature containing topical words is title, which causes information insufficiency problem. Therefore, we propose methods that aim to resolve information insufficiency problem by utilizing web contents. Specifically, the proposed methods the extract main contents from a HTML document collected by using a website URL. Based on the similarity between the title of a conference and its main contents, the topical keywords are selected to enforce the important keywords among the main contents. The experiment results conducted by using a real-world dataset showed that the use of additional information extracted from the conference websites is successful in improving the conference classification performances. We plan to further improve the accuracy of conference classification by considering the structure of websites.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.63-81
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• 2023

This study endeavors to classify and categorize similar policy programs through network clustering analysis, using textual information from data-related policy programs in Korea. To achieve this, descriptions of data-related budgetary programs in South Korea in 2022 were collected, and keywords from the program contents were extracted. Subsequently, the similarity between each program was derived using TF-IDF, and policy program network was constructed accordingly. Following this, the structural characteristics of the network were analyzed, and similar policy programs were clustered and categorized through network clustering. Upon analyzing a total of 97 programs, 7 major clusters were identified, signifying that programs with analogous themes or objectives were categorized based on application area or services utilizing data. The findings of this research illuminate the current status of data-related policy programs in Korea, providing policy implications for a strategic approach to planning future national data strategies and programs, and contributing to the establishment of evidence-based policies.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

• Journal of Korean Library and Information Science Society
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• v.47 no.2
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• pp.51-70
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• 2016

The purpose of this study is to analyze the semantic relation network and valence-arousal dimension through the words that describe emotions in Korean language. The results of this analysis are summarized as follows. Firstly, each emotion word was semantically linked in the network. This particular feature hinders differentiating various types of "emotion words" in accordance with similarity in meaning. Instead, central emotion words playing a central role in a network was identified. Secondly, many words are classified as two categories at the valence and arousal level: (1) negative of valence and high of arousal, (2) negative of valence and middle of arousal. This aspects of Korean emotional words would be useful to analyze emotions in various text data of books and document information.

• Annual Conference of KIPS
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• 2006.05a
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• pp.473-476
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• 2006

인터넷의 발달로 인하여 정보의 양은 시간이 지날수록 폭발적으로 증가하고 있다. 이러한 방대한 정보로부터 정보검색시스템은 사용자에게 너무 많은 검색결과를 제시하여 사용자가 원하는 정보를 찾기 위해 너무 많은 시간을 소요하게 하는 정보의 과적재 문제가 있다. 질의 기반의 문서요약은 정보의 사용자가 원하는 정보의 검색시간을 줄임으로써 정보의 과적재 문제를 해결하는 방법으로서 점차 중요성이 증가하고 있다. 본 논문은 비음수 행렬 인수분해 (NMF, Non-negative Matrix Factorization)과 코사인 유사도를 이용하여 질의 기반의 문서를 요약하는 새로운 방법을 제안하였다. 제안된 방법은 질의와 문서 간에 사전학습이 필요 없다. 또한 문서를 그래프로 변형시키는 복잡한 처리 없이 NMF 에 의해 얻어진 의미 특징(semantic feature)과 의미 변수(semantic variable)로 문서의 고유 구조를 반영하여 요약의 정확도를 높일 수 있다. 마지막으로 단순한 방법으로 문장을 쉽게 요약할 수 있다.

• Genomics & Informatics
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• v.2 no.3
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• pp.134-141
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• 2004

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide and a typical hypervascular tumor. Therefore, it is important to find factors related to angiogenesis in the process of HCC malignancy. In order to find angiogenesis-related factors in HCC, we used combined methods of in silico prediction and an experimental assay. We analyzed 1457 genes extracted from cDNA microarray of HCC patients by text-mining, sequence similarity search and domain analysis. As a result, we predicted that 16 genes were likely to be involved in angiogenesis and then the effects of these genes were confirmed by hypoxia response element(HRE)-luciferase assay. For instant, we classified osteopontin into a potent angiogenic factor and coagulation factor XII into a significant anti­angiogenic factor. Collectively, we suggest that using a combination of in silico prediction and experimental approaches, we can identify HCC-specific angiogenesis­related factors effectively and rapidly.