• Journal of Korean Society of Archives and Records Management
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• v.14 no.4
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• pp.127-151
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• 2014

To search based on the functional classification or provenance is not easy for users, and the key word-based information retrieval presents only simple words matching with the title of the records. The Presidential Archive of Korea developed a subject classification scheme to improve the convenience of searching for various records and came up with a subject thesaurus based on the scheme that utilizes the terms appearing on the title of the records and the terms used by the users who searched the portal or requested information disclosure. This research presents the development process of subject thesaurus. It also presents the utilization methods for records management work and services.

• Journal of Gastric Cancer
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• v.9 no.2
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• pp.46-50
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• 2009

During carcinogenesis, precancers (premalignant lesions) are the morphologically identifiable lesions that precede invasive cancers. In theory, the successful treatment of precancers would result in the eradication of most human cancers. Despite the importance of these lesions, there has been no effort to list and classify all of the precancers. In 2001, the NCI sponsored a workshop on the classification of precancers. When considering all the possible classes of precancers, it is worth noting that not all precancers are neoplastic. In fact, precancers need not progress to cancer, and precancerous lesions often have a high rate of regression. Thus, the following five classes were adopted: 1) acquired microscopic precancers; 2) acquired large lesions with microscopic atypia; 3) Precursor lesions occurring with inherited hyperplastic syndromes that progress to cancer; 4) acquired diffuse hyperplasias and diffuse metaplasias; and 5) currently unclassified entities. In this review paper, precancerous lesions of the stomach are classified and their clinical significance is described.

• The KIPS Transactions:PartD
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• v.19D no.4
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• pp.263-270
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• 2012

With the current development of high speed Internet and massive database technology, the amount of web documents increases rapidly, and thus, classifying those documents automatically is getting important. In this study, we propose an effective method to extract document features based on Hangeul morpheme and keyword analyses, and to classify non-structured documents automatically by predicting subjects of those documents. To extract document features, first, we select terms using a morpheme analyzer, form the keyword set based on term frequency and subject-discriminating power, and perform the scoring for each keyword using the discriminating power. Then, we generate the classification model by utilizing the commercial software that implements the decision tree, neural network, and SVM(support vector machine). Experimental results show that the proposed feature extraction method has achieved considerable performance, i.e., average precision 0.90 and recall 0.84 in case of the decision tree, in classifying the web documents by subjects.

• KLA journal
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• v.13 no.1
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• pp.30-32
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• 1972

• KLA journal
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• v.11 no.12
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• pp.37-40
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• 1970

• Proceedings of the Korea Database Society Conference
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• 2001.06a
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• pp.295-302
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• 2001

최근 대량의 텍스트 문서로부터 의미 있는 패턴이나 연관 규칙을 발견하기 위한 텍스트마이닝 기법에 대한 연구가 활발히 전개되고 있다. 하지만 비정형 텍스트 문서로부터 추출된 용어의 수는 불규칙적이고 일반적인 용어가 많이 추출되는 관계로 기존의 연관 규칙 탐사 방법을 사용하게 되면 무의미한 연관 규칙이 대량으로 생성되어 지식 정보를 효과적으로 검색하기 어렵다. 본 논문에서는 연관 규칙 탐사 기법을 이용하여 비감독학습 기법에 의해 대량의 문서를 효율적으로 분류하기 위한 대표 색인어 추출 기법을 제안하였다. 컴퓨터 분야의 논문을 대상으로 각 분야별 대표 색인어를 추출하여 유사한 문서끼리 분류하는 실험을 통해 제안된 방법의 효율성을 보였다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.61-63
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• 2006

본 논문은 온톨로지를 이용하여 단백질 상호작용 네트워크를 개념적으로 분류하여 레이아웃하는 방법을 제안한다. 상호작용 네트워크를 이루는 단백질은 온톨로지의 표준 통제 용어에 대한 주석 정보를 가지고 있으므로 동일 분류에 해당하는 통제 용어를 가지고 있는 단백질들은 근접한 곳에 위치하도록 레이아웃한다. 이는 기존 물리적 레이아웃에 기능별 그룹화를 해줌으로써 복잡한 네트워크를 개념적으로 분석할 수 있도록 한다. 또한, 동일 분류에 속하는 단백질들을 한 노드로 대응하여 레이아웃 알고리즘을 수행함으로써 기존의 그래프표현 알고리즘 보다 빠르게 시각화할 수 있다.

• Journal of The Korean Association For Science Education
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• v.40 no.3
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• pp.321-336
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• 2020

We explore automated scoring models of scientific argumentation. We consider how a new analytical approach using a machine learning technique may enhance the understanding of spoken argumentation in the classroom. We sampled 2,605 utterances that occurred during a high school student's science class on molecular structure and classified the utterances into five argumentative elements. Next, we performed Text Preprocessing for the classified utterances. As machine learning techniques, we applied support vector machines, decision tree, random forest, and artificial neural network. For enhancing the identification of rebuttal elements, we used a heuristic feature-engineering method that applies experts' classification of morphemes of scientific argumentation.

• The Journal of Korean Association of Computer Education
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• v.11 no.3
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• pp.67-80
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• 2008

Various document categorization methods have been studied to provide a user with an effective way of browsing a large scale of documents. They do compares set of documents into groups of semantically similar documents automatically. However, the automatic categorization method suffers from low accuracy. This thesis proposes a semi-automatic document categorization method based on the domains of documents. Each documents is belongs to its initial domain. All the documents in each domain are recursively clustered in a level-wise manner, so that the category tree of the documents can be founded. To find the clusters of documents, the stop-word of each document is removed on the document frequency of a word in the domain. For each cluster, its cluster keywords are extracted based on the common keywords among the documents, and are used as the category of the domain. Recursively, each cluster is regarded as a specified domain and the same procedure is repeated until it is terminated by a user. In each level of clustering, a user can adjust any incorrectly clustered documents to improve the accuracy of the document categorization.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.181-182
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• 2019

국가과학기술지식정보서비스(NTIS)는 국가R&D 과제정보를 중심으로 참여인력, 성과(물), 참여기관 등의 정보를 연계하여 제공하고 있다. 각 과제정보는 한글 및 영문 키워드와 과학기술표준분류를 포함하고 있어, 과제정보를 중심으로 한 국가R&D정보 검색 및 분류에 활용하기 적합하다. 이러한 국가R&D정보를 서비스함에 있어 단순 검색을 벗어나 다양한 형태로 가공된 정보를 제공하기 위해서는 국가R&D 정보에 적합한 과학기술용어사전 구축이 필수적이다. 본 논문에서는 국가R&D 과제 키워드를 활용해 국가R&D정보에 적합한 과학기술용어사전을 구축하는 방안을 제안하고자 한다.