• Journal of KIISE
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• v.44 no.2
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• pp.139-147
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• 2017

Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the "big data" era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors' experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.

• Proceedings of the Korean Society for Information Management Conference
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• 2003.08a
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• pp.245-251
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• 2003

이 연구에서는 통제된 색인어를 이용하여 파악한 지식 구조와 통제되지 않은 키워드를 이용한 지식 구조를 비교하여 두 구조가 어떤 차이점을 보이는지를 살펴보았다. 또한 색인효과가 어떻게 나타나는지, 비통제어를 사용한 경우가 실제적으로 더 상세한 하위 영역을 표현하는지를 확인하고자 하였다. 실험 결과 통제된 색인어인 주제명표목을 사용한 영역지도와 비통제 색인어인 키워드를 사용한 영역지도 둘 다 인공지능 분야의 주요 분야들을 비슷하게 나타냈지만, 주제명표목을 사용한 경우에 색인효과가 일부 나타났다. 그리고 대체적으로 주제명표목에 기반한 영역지도보다는 키워드에 기반한 영역지도가 더 상세하게 나타났다.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.1-17
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• 2012

The Smartphone, one of essential mobile devices widely used recently, can be very effectively applied to capture knowledge on the spot by jointly applying the pervasive functionality of cloud computing. The process of knowledge capturing can be also effectively automated if the topic of knowledge is automatically identified. Therefore, this paper suggests an interdisciplinary approach to automatically acquire knowledge on the spot by combining technologies of text mining-based topic identification and cloud computing-based Smartphone. The Smartphone is used not only as the recorder to record knowledge possessor's dialogue which plays the role of the knowledge source, but also as the sensor to collect knowledge possessor's context data which characterize specific situations surrounding him or her. The support vector machine, one of well-known outperforming text mining algorithms, is applied to extract the topic of knowledge. By relating the topic and context data, a business rule can be formulated, and by aggregating the rule, the topic, context data, and the dictated dialogue, a set of knowledge is automatically acquired.

• Journal of the Korean Society for information Management
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• v.28 no.1
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• pp.171-193
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• 2011

This study aims to analyse the necessity of the subject heading construction for the word based classification and to suggest a methodology that uses various knowledge organization systems(KOS). For this purpose, six kinds of KOS were collected for the 20 selected works in each subject. The collected subjects were analysed in terms of constructing a subject heading for the word based classification. The result of the analysis shows that there is a noticeable difference between the library oriented KOS and commercial oriented KOS. In addition, user oriented tags are more similar to the commercial sector's concerning subject categorization than the library oriented ones. However, there is no noticeable difference among the library oriented KOS, commercial sector oriented KOS, and user oriented tags regarding the subject vocabulary. Some practical implications were suggested for the application to the Korean libraries based on the findings of this study.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.68-74
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• 2019

The purpose of this study was to identify research trends of 892 domestic articles (2009-2018) related to emotional labor by using text-mining and network analysis. To this end, the keyword of these papers were collected and coded and eventually converted to 871 nodes and 2625 links for network text analysis. First, network text analysis revealed that the top four main keyword, according to co-occurrence frequency, were burnout, turnover intention, job stress, and job satisfaction in order and that the frequency and the top four core keyword by degree centrality were all relatively the high. Second, based on the top four core keyword of degree centrality the ego network analysis was conducted and the keyword for connection centroid of each network were presented.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.11a
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• pp.459-468
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• 2017

본 논문에서는 자율주행자동차와 관련한 주제어를 선정하여 KCI 등재 논문의 서론 자료를 수집하고, 이에 빅데이터 분석 기법을 적용하였다. 이를 토대로 자율주행자동차와 관련된 다양한 이슈 분석을 통해 자율주행자동차의 연구 동향을 파악할 수 있으며, 추가적인 연구가 필요한 분야에 대해 알 수 있다. 제4차 산업혁명의 영향으로 등장한 다양한 기술들의 활용이라고 볼 수 있는 자율주행자동차는 2025년 상용화 될 가능성이 높다. 자율주행자동차의 상용화를 위해 지속적인 연구와 논의가 필요하지만, 과거부터 등재된 자율주행자동차 관련 KCI 논문 빅데이터 분석을 통해 기술들 간의 군집 방식과 주제어의 밀집도, 네트워킹 형성 방식 등에 대해 파악할 수 있다. 이처럼 논문 데이터 분석을 통해 향후 정부출연(연), 혹은 기업체에서 더욱 발전시켜야 할 부분에 대해 인지하고 정부 차원의 과제 지원과 연구를 통해 자율 주행자동차 상용화를 촉진시킬 수 있을 것이라고 예상한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.526-529
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• 2018

본 연구에서는 텍스트 네트워크분석을 활용하여 국방분야 연구의 핵심 주제어와 연구주제를 분석하고 이를 통해 전체 지식구조를 파악하고자 하였다. 이를 위해 2010년부터 2017년까지의 국방대학교 학위과정 논문을 대상으로 국방분야 연구현황을 진단하고 지식구조를 구성하였다. 8년간 누적된 논문 710건의 초록을 분석하여 총 6,883개의 단어를 추출한 후, 단어의 논문 등장 빈도수와 단어간 링크수를 파레토 법칙에 따라 상위 20%의 기준으로 총 270개의 단어로 추출하였고, 컴포넌트 분석을 통해 최종 170개의 핵심 주제어를 도출하였다. 이 핵심 주제어를 통해 중심성 분석과 응집구조를 분석하여, 국방분야에 대한 총 6개의 지식구조 그룹을 도출하였다.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.47-56
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• 2018

The aim of this study is investigate the research trends by analyzing the researches related to Korean and international genetics in oncology nursing. We conducted a text network analysis focusing on the key words presented in the abstracts of papers published in journals related to genetics in oncology nursing. Nurse, Cancer, Genetic, Patient, Knowledge, Care, and Genetic Test were identified as keywords and centralized keywords. As a result of studying research trends over time, researches including keywords such as information, care, and knowledge have increased since the completion of the Human Genome Project in 2003. Key words classified through the meta paradigm of nursing were health, nursing, human, environment order. This study is meaningful in that it can be used to identify trends in tumor genetic nursing research and to set the direction of development of nursing intervention for hereditary cancer patients.

• Journal of KIISE:Software and Applications
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• v.29 no.10
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• pp.747-754
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• 2002

In this paper, We propose a document thematic words extraction by using principal component analysis(PCA) which is one of the multivariate statistical methods. The proposed PCA model understands the flow of words in the document by using an eigenvalue and an eigenvector, and extracts thematic words. The proposed model is estimated by applying to document summarization. Experimental results using newspaper articles show that the proposed model is superior to the model using either word frequency or information retrieval thesaurus. We expect that the Proposed model can be applied to information retrieval , information extraction and document summarization.

• Journal of agricultural medicine and community health
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• v.35 no.2
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• pp.134-150
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• 2010

Objectives: This study was for analyzing the research about international marriage immigrant women and a trial to find the right direction for future research. Methods: Sixty articles published from June, 2004 to June, 2009 were reviewed and analyzed according to the general characteristics, major of author, and theme of health domains. Results: Most of them were master's thesis(71.7%) and journals(21.7%) and doctoral dissertation(6.7%) have been published mostly after thesis. Among 83.3% for quantitative research, descriptive(33.3%) and descriptive correlation(41.7%) methods were the most used and there were some qualitative researches(16.7%). The most frequently used data gathering method was questionnaire(81.7%) and the next was interview(16.7%). The major rates of the author were 61.7% for social welfare and 2.1% for nursing. The investigated variables in social health domain were adaptation(28.3%), and communication(1.7%). In psychological health domain, marriage satisfaction(16.7%), life satisfaction(11.7%), and depression(10.0%) were most researched. Utilization of medical center(5.0%) and health promotion behavior(1.7%) were investigated in physical health domain. Conclusions: Above this, most articles were researched about the adaptation of international marriage immigrant women. But the life in foreign countries can cause physical and psychosocial unhealthy conditions, so many-sided health related researches are supposed to be conducted for adaptation and prevention health problems of international marriage immigrant women.