• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.535-537
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• 2005

본 논문에서는 시소러스 상에서 개념간 세분화를 위한 의미적 기준인 개념 패싯 (Conceptual Facet)과 관계 패싯 (Relational Facet) 그룹을 사용하는데, 패싯이란 공통의 특성을 갖는 개념들을 함께 그룹화하고 용어간 관계를 구조화하기 위한 장치이다. 개념 패싯은 용어가 갖는 대표적인 의미속성, 범주를 의미하며, 용어 각각을 구별되는 의미장 (Semantic Field)에 분류하도록 한다. 관계 패싯은 상$\cdot$하위 개념 간 의미 관계를 표현한 메타 개념이다. 본 시소러스는 여러 전문가들의 다양한 관점을 반영하도록 설계되었다. 관점이란 주관적이며, 임의적이어서 개별 개념에 내재된 자질 또는 속성과는 구별되는 독립적 속성이다. 개념 패싯, 관계 패싯의 도입은 계층 관계, 동등 관계, 범주 관계 등과 더불어 용어 간 관계를 보다 구체적으로 명시함으로써 최종 사용자에게 검색의 효율성과 정확성을 제공할 수 있다.

• Proceedings of the Korean Society for Information Management Conference
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• 2014.08a
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• pp.119-122
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• 2014

본 연구는 교육학 분야에서 국내 학술 논문 저자키워드 출현 순서에 의미가 있는지 알아보기 위하여 저자키워드 출현 순서별로 빈도를 조사하고 동 저자 키워드가 제목에 출현하는지를 살펴본 후, 학술논문 저자키워드 출현 순서에 따른 빈도가 저자키워드의 논문 제목 출현 빈도에 미치는 영향에 대해 연구하고자 한다. 연구의 배경은 구조적 학술용어사전의 구축과 관련해서 효율적인 용어 선정이나 용어 분류 방법을 찾는 것이다. 연구의 목적을 위해 교육학 분야 저자키워드의 출현 빈도수가 1인 것을 제외하고 고빈도 순으로 400개 용어를 추출하였다. 저자키워드의 몇 번째 출현 순서가 저자키워드의 제목 출현에 가장 큰 영향을 미치는지 알아본 결과, 저자키워드의 첫 번째 출현이 아닌 두 번째 출현이 가장 큰 영향을 미친다는 것을 알 수 있었다.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2023.11a
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• pp.311-313
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• 2023

군중눌림 사고 형태의 재난은 국내에서 경험과 연구가 미진한 재난의 형태이지만 세계적으로는 선진국이나 후진국을 가리지 않고 꾸준히 발생중이며, 2022년 10월 29일의 이태원 참사에서 보듯이 국가사회에 엄청난 파급력과 영향을 주는 재난이다. 따라서 군중눌림 사고를 이해하고 발전된 학술적 접근 방식을 통하여 최소한의 예방, 대비, 대응 수준에 이르러야 하나 국내에서는 그 연구가 미진하고 용어와 개념 정립도 되어 있지 않다. 군중눌림 사고는 그 본질 상 얼마 안 되는 짧은 골든 타임 내에 해결이 되지 않으면 대처 시간이 거의 없어 인명피해 대처가 매우 어렵다. 즉 질식으로 의식이 저하되는 피해자가 신고되어도 현장 도착 구급대원이 밀집된 군중을 뚫고 피해자에게 신속히 접근하기란 애무 어려워, 선진국들조차도 많은 인명피해를 내지만 뚜렷한 한 가지 해결책을 제시 못하고 있는 형편이다. 이러한 상황에서 군중압박 사고 관련 정립되지 않은 용어를 관련 개념과 같이 조사 정리하고, 과거 있었던 대표적 군중집회의 분류, 특성 등을 알아보며, 이를 기반으로 용어와 개념이 정리 및 표준화되는 기반이 되도록 하였다.

• Journal of Korean Library and Information Science Society
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• v.35 no.4
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• pp.171-191
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• 2004

This study presents revision scheme of Korean Decimal Classification appropriate for classification of children-related materials, mainly centered on social science(300) and pure science(400) occupying the majority of children-related materials in school Libraries. Towards this goal, 1 have studied the development and use of classification system for children-related materials available in domestic and overseas school libraries or children's libraries, and researched elementary school 4th, 5th, and 6th grade students' degree of understanding on classification item terms and children-related materials terms used for KDC's social science and Pure science. Based on the results of analysis, f have presented revision scheme of Korean Decimal Classification item terms and class numbers for children-related materials.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.4
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• pp.281-299
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• 2013

The purpose of this study is to suggest improvements of pharmacy field in the KDC 6th edition. To achieve this purpose, first, this study reviewed academic area and system of pharmacy. Second, we analyzed headings of pharmacy field from first edition to 6th edition. Third, we analyzed change process of classification items in pharmacy field. Forth, we investigated general problems concerning pharmacy field in the KDC 6th edition based on the comparative analysis with DDC, NLMC, and Research Field Code by Korean Research Foundation. As a result, pharmacy field of KDC has never changed since its 4th edition. And then, this study was added new subjects related to sections and subsections of pharmacy and suggested modern terminology. Also newly assigned pharmaceutics industry, cancer treatment and biological pharmaceutics fields under 518.7-.8 which do not use in pharmacy field for efficient classification.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.837-843
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• 2001

In this work, we propose a new method of extracting and weighting representative keywords(RKs) from a few documents that might interest a user. In order to extract RKs, we first extract candidate terms and them choose a number of terms called initial representative keywords (IRKs) from them through fuzzy inference. Then, by expanding and reweighting IRKs using term co-occurrence similarity, the final RKs are obtained. Performance of our approach is heavily influenced by effectiveness of selection method of IRKs so that we choose fuzzy inference because it is more effective in handling the uncertainty inherent in selecting representative keywords of documents. The problem addressed in this paper can be viewed as the one of calculating center of document vectors. So, to show the usefulness of our approach, we compare with two famous methods - Rocchio and Widrow-Hoff - on a number of documents collections. The result show that our approach outperforms the other approaches.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.449-456
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• 2019

This study attempted to improve the quality of hairstyling-related studies and provide basic data for future studies on hairstyling terms through analysis of cosmetology-related loan words used in hairstyling theses among recent cosmetology papers. For data collection to derive valid conclusions, the signatures of a total of 1,980 academic papers collected after typing in the keyword 'Hair' at the Research Information Sharing Service (http://www.riss.kr) were analyzed. The results show that researchers in hairstyling seem not to pay close attention to the correct use of foreign loan words. Therefore, the study results would be very helpful to the development of future cosmetology studies. The correct notation and use of foreign loanwords should be further encouraged.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.35-40
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• 2022

In this paper, we propose a method of constructing clinical knowledge with clinical concept relations and predicting diseases based on a deep learning model to support clinical decision-making. Clinical terms in UMLS(Unified Medical Language System) and cancer-related medical knowledge are classified into five categories. Medical related documents in Wikipedia are extracted using the classified clinical terms. Clinical concept relations are established by matching the extracted medical related documents with the extracted clinical terms. After deep learning using clinical knowledge, a disease is predicted based on medical terms expressed in a query. Thereafter, medical terms related to the predicted disease are selected as an extended query for clinical document retrieval. To validate our method, we have experimented on TREC Clinical Decision Support (CDS) and TREC Precision Medicine (PM) test collections.

• Journal of The Korean Association For Science Education
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• v.34 no.2
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• pp.55-62
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• 2014

This study is to reclassify the classifications or definitions of scientific literacy in scientific literacy researches since 1960s and grasp the classification trends of scientific literacy definitions. Sixteen articles have been selected among the articles that have been introduced in the two articles. Classification criteria are as follows: 1) "be learned," "competence," or "be able to function in society" as meanings of "literate," 2) "terms" or "description" as the ways of representing scientific literacy, 3) "singular structure," "hierarchical structure," or "parallel structure" as the inner structure of scientific literacy definitions. The results of this study are as follows: First, hierarchical structures in scientific literacy have almost always accompanied "terms" representing scientific literacy and also accepted the hierarchy between "be learned" and "competence," but not the definition of scientific literacy as functioning in society. All parallel structures in scientific literacy have accompanied the definition as functioning in society. And singular structure almost always appears in researches based on the views of scientific literacy in relatively recent times. Second, researches who have used "terms" as ways of representing scientific literacy have increased. Based on the results in this study, the meanings of scientific literacy have been emphasized in view of the ability of playing a role in a social context as well as learning and competence these days. To meet this movement in scientific literacy actively, science education community should get out of traditional teaching and learning scientific concepts and give emphasis on application in various context and social role of science learners.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.475-477
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• 2002

자동 문서 분류는 범주 특성 벡터와 입력 문서 벡터의 유사도 비교에 의해 가장 유사한 범주를 선택하는 방법이다. 문서 분류 시스템을 구현하기 위하여 각 범주의 특성 벡터를 정보 검색 시스템의 역파일 형태로 구축하였으며, 용어 가중치를 계산하는 방법을 달리하여 문서 분류 시스템의 정확도를 실험하였다. 실험 문서는 일간지의 신문기사들을 무작위로 추출한 문서 집합을 대상으로 하였으며, 정보 검색 모델에서 보편적으로 사용되는 TF-lDF 방식이 변형된 방식에 비해 더 나은 성능을 보였다.