• Asia pacific journal of information systems
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• v.14 no.2
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• pp.115-130
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• 2004

This paper proposes how to build an expert profile database in KMS, which provides the information of expertise that each expert possesses in the organization. To manage tacit knowledge in a knowledge management system, recent researches in this field have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise so that users can contact them for help. In this paper, we develop a framework to automate expert classification using a text categorization technique called Vector Space Model, through which an expert database composed of all the compiled profile information is built. This approach minimizes the maintenance cost of manual expert profiling while eliminating the possibility of incorrectness and obsolescence resulted from subjective manual processing. Also, we define the structure of expertise so that we can implement the expert classification framework to build an expert database in KMS. The developed prototype system, "Knowledge Portal for Researchers in Science and Technology," is introduced to show the applicability of the proposed framework.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.3
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• pp.151-173
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• 2006

The purpose of this study is to make out a draft for the standard classification of the National Knowledge and Information Resources. As the result of the Study the standard classification system of the national knowledge and information resources, named "Knowledge Classification 'KC' is suggested. KC consists of 3 classification systems classification by subject, type of resources and type of media. The classification by subject has 12 main classes, and each main class has divisions. Main classes consist each of major discipline or group of related disciplines. The type of resources is classified by 10 types of content, likewise numbered 0-9, and the media of knowledge are classified by 8 types. likewise 0-7. In the Practice the notation always consists of 2 characters and 2 digits. The first character designate main class and the second character designate division. The first number designate the type of resources and the second number designate the type of media.

• Journal of the Korean Society for Library and Information Science
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• v.48 no.4
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• pp.5-23
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• 2014

This study reports the first step in the Classification-based Search and Knowledge Discovery (CSKD) project, which aims to combine information organization and retrieval approaches for building digital library applications. In this study, we explored the generation and application of a faceted vocabulary as a potential mechanism to enhance knowledge discovery. The faceted vocabulary construction process revealed some heuristics that can be refined in follow-up studies to further automate the creation of faceted classification structure, while our concept search application demonstrated the utility and potential of integrating classification-based approach with retrieval-based approach. Integration of text- and classification-based methods as outlined in this paper combines the strengths of two vastly different approaches to information discovery by constructing and utilizing a flexible information organization scheme from an existing classification structure.

• Journal of the Korean Society for Library and Information Science
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• v.12
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• pp.201-224
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• 1985

The traditional oriental materials are very important to study on Oriental or Korean studies. Every reseacher that study on this field is familier to Four Category Classification Scheme (四部分類法) as it is based on the traditional knowledge of Orient. Then, when all materials of libraries will he computerized, it will be the first condition that will has to understand about the classification of division and section of oriental knowledge, because not only ancient literature but also many dissertation of this subject will be classified. Therefore, Four Category Classification Scheme has been valuable until now. This paper is intended to help librarians to classify the traditional oriental materials or the dissertation concerned with that, to serve researched user that literatures which have been filed among various traditional bibliographies. The outline of this study are as follows: :1 Examining closely origins, developing process and characteristics of classification of Confacian Classics Part (經部) of Four Category Classification Scheme. (2) Explaning the content of division and section of Confucian Classics Part (經部). (3) Coordinating relation of division and section of Confucian Classics Part as well as those of other parts of the classification scheme. (4) Clearing up the limitation of classification related to other division. (5) Attempting to give basic knowledge on practical classification as concrete examples beloging to each division and section of classification.

• Journal of Korean Library and Information Science Society
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• v.39 no.3
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• pp.369-386
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• 2008

The purpose of this study is investigate to compare with knowledge classification and library classification system of botany. First, the knowledge field of botany is mainly classified in morphology, physiology, ecology, taxonomy, genetics, evolution and others by the study object of plants. Second, the division of plants is treated in the field of taxonomy, that is, a lower subdivision study of botany, and Engler's classification is still prevalent in the taxonomy. Third, in library classification, KDC, NDC, UDC and CC adopted the Engler's classification, but DDC and LCC was taken of the Bentham & Hooker's classification. In the Engler's classification, plants are arranged by evolution's order, from lower vegetation to higher vegetation, but Bentham & Hooker's classification is arranged in the reverse order. Forth, it is desirable that every plants(482-489) of KDC' botany are subdivided by the attribute or structure of plants being treated in the general botany as if they are subdivided in the DDC or CC.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.672-674
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• 2000

There are various ways in classification methodologies of data mining such as neural networks but the result should be explicit and understandable and the classification rules be short and clear. Rough set theory is a effective technique in extracting knowledge from incomplete and inconsistent information and makes an offer classification and approximation by various attributes with effect. This paper discusses granularity of knowledge for reasoning of uncertain concepts by using generalized rough set approximations based on hierarchical granulation structure and uses hierarchical classification methodology that is more effective technique for classification by applying core to upper level. The consistency rules with minimal attributes is discovered and applied to classifying real data.

• The Journal of Korean Medical History
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• v.19 no.1
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• pp.65-76
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• 2006

The present study examined subject classification and the characteristics of old Oriental medicine literature focused on Web services of Oriental medicine knowledge and information resources. For this, we reviewed how subject classification is applied to Oriental medicine in the codified literature classification table and, based on the results, examined how the classification system is used in libraries. Second, subject classification and the characteristics of old Oriental medicine literature were studied focused on Web services of Oriental medicine knowledge and information resources, and related problems and solutions were suggested.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• Journal of Korea Technology Innovation Society
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• v.10 no.3
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• pp.531-554
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• 2007

This paper suggests a new model connecting various knowledge activities through classification systems such as classifications of research, technology, industry, major and job. Although research activities are linked to technology and industry areas or to education and job areas, there is no effort to link these kinds of activities. There are a few studies to link research and technology or research and education respectively. But, there have been no studies to connect technology-industry linkage and education-job linkage. This paper suggests that research area can be a basis of link between technology-industry linkage and education-job linkage. The methods building the links are not simple, but easy; 1) setting up new science/research classification system having two dimensions of research and application, 2) building electronic systems and databases allowing fields for several classification systems, and 3) making rules using multi-dimensional classification systems following the purpose of the programs. The model is designed to meet the needs of nationwide R&D and human resources policies, and for the preparation of knowledge society to grasp the relationship between sequential activities using knowledge. If we know the interactive relationships between various areas, we can trace related phenomena in different activities with restricted information.

• Journal of the Korean Geographical Society
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• v.43 no.5
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• pp.761-774
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• 2008

Integration of GIS data and human expert knowledge into digital image processing has long been acknowledged as a necessity to improve remote sensing image analysis. We propose inductive machine learning algorithm for GIS data integration and rule-based classification method for land cover classification. Proposed method is tested with a land cover classification of a Landsat ETM+ multispectral image and GIS data layers including elevation, aspect, slope, distance to water bodies, distance to road network, and population density. Decision trees and production rules for land cover classification are generated by C5.0 inductive machine learning algorithm with 350 stratified random point samples. Production rules are used for land cover classification integrated with unsupervised ISODATA classification. Result shows that GIS data layers such as elevation, distance to water bodies and population density can be effectively integrated for rule-based image classification. Intuitive production rules generated by inductive machine learning are easy to understand. Proposed method demonstrates how various GIS data layers can be integrated with remotely sensed imagery in a framework of knowledge base construction to improve land cover classification.