• Journal of Korean Library and Information Science Society
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• v.39 no.3
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• pp.345-368
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• 2008

This study investigated the general problems concerning the subject Computer Science in the KDC(Korean Decimal Classification) 4th edition based on the comparative analysis with DDC, NDC, Disciplinary Classification System of Korean Research Foundation and National Standard Science and Technology Classification and Science and Technology Classification of Korea Science and Engineering Foundation, and suggested some ideas for the improvements of them. The subject of Computer science in the KDC 4th edition will be helpful to be improved to integrate in classes 004-005 now separated into two main classes of 000(004-005) and 500(566) in KDC4, to systematize subdivisions, to add new subjects, to delete and relocate some inappropriate subjects and to add notes.

• Archives of design research
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• v.17 no.4
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• pp.79-88
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• 2004

This study is aimed to grasp the organization of Internet information resources and to infer the characteristics of resource search engines so that criteria may be established to classify and evaluate Internet information resources. In addition, the author has compared and analyzed interior design classification systems of directory sites of each subject that provide classification system based on the Internet, foreign sites to be used to search for information, and domestic information-specialized sites in order to set up models of interior design classification systems of directories of each Web subject. The systems have been analyzed against such four measures as comprehensiveness of the subject scope, logicality of classification systems, preciseness of subject terms, and effectiveness of searches. Information of interior designs is mixed with that of related fields, and so its information search and classification are not organized systematically. The author has analyzed such a problem so as to present models of search engine classification systems for interior design information classification after considering both academic and practical aspects.

• The KIPS Transactions:PartD
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• v.12D no.5 s.101
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• pp.789-796
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• 2005

Because existing information retrieval systems, in particular library retrieval systems, use 'exact keyword matching' with user's query, they present user with massive results including irrelevant information. So, a user spends extra effort and time to get the relevant information from the results. Thus, this paper will propose SULRM a Retrieval Model using Subject Classification Table, User profile, and LSI(Latent Semantic Indexing), to provide more relevant results. SULRM uses document filtering technique for classified data and document ranking technique for non-classified data in the results of keyword-based retrieval. Filtering technique uses Subject Classification Table, and ranking technique uses user profile and LSI. And, we have performed experiments on the performance of filtering technique, user profile updating method, and document ranking technique using the results of information retrieval system of our university' digital library system. In case that many documents are retrieved proposed techniques are able to provide user with filtered data and ranked data according to user's subject and preference.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.26.1-26.15
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• 2023

Background: The objective of this study is to investigate the differences in incidence rates of targeted diseases by classification of occupations among construction workers in Korea. Methods: In a subject-based cohort of the Korean Construction Worker's Cohort, we surveyed a total of 1,027 construction workers. As occupational exposure, the classification of occupations was developed using two axes: construction business and job type. To analyze disease incidence, we linked survey data with National Health Insurance Service data. Eleven target disease categories with high prevalence or estimated work-relatedness among construction workers were evaluated in our study. The average incidence rates were calculated as cases per 1,000 person-years (PY). Results: Injury, poisoning, and certain other consequences of external causes had the highest incidence rate of 344.08 per 1,000 PY, followed by disease of the musculoskeletal system and connective tissue for 208.64 and diseases of the skin and subcutaneous tissue for 197.87 in our cohort. We especially found that chronic obstructive pulmonary disease was more common in construction painters, civil engineering welders, and civil engineering frame mold carpenters, asthma in construction painters, landscape, and construction water proofers, interstitial lung diseases in construction water proofers. Conclusions: This is the first study to systematically classify complex construction occupations in order to analyze occupational diseases in Korean construction workers. There were differences in disease incidences among construction workers based on the classification of occupations. It is necessary to develop customized occupational safety and health policies for high-risk occupations for each disease in the construction industry.

• Journal of Korean society of Dental Hygiene
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• v.21 no.2
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• pp.193-201
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• 2021

Objectives: We evaluated the learning objectives of dental hygiene courses based on Bloom's learning objectives, and analyze the degree of match with the dental hygienist's job for each detailed subject. Methods: The 5th edition of 'Dental hygiene and learning objectives' was analyzed by subject based on Bloom's cognitive domain classification from March 10 to April. In addition, the degree of match between the contents of the secondary job analysis of the dental hygienist and the learning objectives for each detailed subject were analyzed. Results: The total number of dental hygiene learning objectives was 2,975 (2,762 theory, 52 practice). Among the cognitive domains, the comprehension domain was the most common (79.8%), and the skill domain was very low (4.9%). In the job for each detailed subject of dental hygiene, the most frequently performed was 'dental prophylaxis and practice' with 103 subjects. Conclusions: Overall, dental hygiene learning objectives are mostly theory-oriented, so it is necessary to expand and improve in the direction related to the jobs that clinical dental hygienists perform in the field. In addition, it is necessary to continuously develop timely learning goals, and prepare active strategies for developing high-quality items.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.4
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• pp.71-84
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• 2017

The paper discusses the nature of Knowledge Organization Systems (KOSs) and shows how these can support digital library users. It demonstrates processes related to integration of KOS like the Dewey Decimal Classification, $22^{nd}$ edition (DDC22) in DSpace software (http://www.dspace.org/) for organizing and retrieving (browsing and searching) scholarly objects. An attempt has been made to use the DDC22 available in Bengali language and highlights the required mechanisms for system-level integration. It may help a repository administrator to build an IDR (Institutional Digital Repository) integrated with SKOS-enabled multilingual subject access systems for supporting subject descriptors based indexing (DC.Subject metadata element), structured navigation (browsing) and efficient searching.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.325-332
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• 2014

It is generally accepted that classification accuracy is affected by the number of learning documents, but there are few studies that show how this influences automatic text classification. This study is focused on evaluating the deviation-based classification model which is developed recently for genre-based classification and comparing it to other classification algorithms with the changing number of training documents. Experiment results show that the deviation-based classification model performs with a superior accuracy of 0.8 from categorizing 7 genres with only 21 training documents. This exceeds the accuracy of Bayesian and SVM. The Deviation-based classification model obtains strong feature selection capability even with small number of training documents because it learns subject information within genre while other methods use different learning process.

• Journal of the Korean Society for information Management
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• v.39 no.4
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• pp.347-373
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• 2022

In this study, in order to expand the access points of reading materials, a reading material classification (RMC) system based on the facets of book use was devised. The facets of books that can be considered by book users in the reading situation were content-analyzed. Also, through network analysis, subject headings adjacent to one subject heading were grouped into related subject headings. The RMC developed in this study can be used as a tool that provides various access points to help book users search in the library OPAC and other reading information systems.

• Journal of Korean Society of Archives and Records Management
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• v.5 no.2
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• pp.73-101
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• 2005

This study aims to identify the developing process of classification shemes for NGO records. And it chooses the KFEM(Korea Federation for Environmental Movenment) for case study, which is a representative NGO of Korea. This study proposes the classification principles in the form that the function classification and subject classification are combined. The development model of function classification schemes on the KFEM records is based on the Australian Standard Work Process Analysis for Recordkeeping(AS 5090) and the DIRKS (Designing and Implementing Recordkeeping Systems) methodology. Literature review, interviews, work process analysis, and questionnaire surveys have been employed as research methodology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6080-6096
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• 2019

Text classification is one of the fundamental techniques in natural language processing. Numerous studies are based on text classification, such as news subject classification, question answering system classification, and movie review classification. Traditional text classification methods are used to extract features and then classify them. However, traditional methods are too complex to operate, and their accuracy is not sufficiently high. Recently, convolutional neural network (CNN) based one-hot method has been proposed in text classification to solve this problem. In this paper, we propose an improved method using CNN based skip-gram method for Chinese text classification and it conducts in Sogou news corpus. Experimental results indicate that CNN with the skip-gram model performs more efficiently than CNN-based one-hot method.