• 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 BIBLIA Society for library and Information Science
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• v.21 no.2
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• pp.69-80
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• 2010

This study analyzed the Harris's philosophical ideas, American library classification in the Harris's era, and relationship to Bacon, Hegel, DDC based on the Harris's classification system. As is generally known so far, Harris himself described to have derived his classification from inverted Baconian arrangement. It is nowhere to be found that Harris reflected on Hegel's philosophical ideas in his scheme. But on this study, Harris was a Hegelian and a leading American exponent of ideas of Hegel at that time. So we can analogize being reflected on Hegelian philosophy in the Harris's scheme and actually the Hegelian basis can be founded for much of Harris's scheme. Also we can find that Harris's scheme springs directly from Bacon and indirectly Johnston's scheme.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.1
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• pp.333-351
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• 2011

As the boundaries between disciplines have been weakened and interdisciplinary or multidisciplinary subjects have increased, there is a limitation to express subjects with DDC. This study evaluates DDC instructions using interdisciplinary numbers and classifying more than one discipline through Delphi research. As a result, it is found that there is a need for defining separately 'interdisciplinary subjects' and 'multidisciplinary subjects', based on the level of integration among disciplines, and improving the instruction to maintain the consistency in assigning and filing classification numbers. It is also discussed that to enhance the symbols' power of expression, an analytic/synthetic method could be adopted.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.21 no.1
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• pp.43-57
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• 2010

The purposes of this study are to examine how food and culture and Korean foods are reflected in the classification systems and to propose improvements of DDC to classify various subjects related to the materials of food and culture. For the study, six classification systems - DDC(Dewey Decimal Classification), UDC(Universal Decimal Classification), LCC(Library of Congress Classification), KDC(Korean Decimal Classification), NDC (Nippon Decimal Classification), China Library Classification - were analyzed in aspects of eating and drinking customs, eating etiquette, nutrition and diet, food and drink, meal and table service, beverage technology, and food technology. As a result, there were few headings about Korean food in six classification systems and it was necessary for DDC to have new headings for classifying Korean and Asian traditional foods and table services. Due to the literary warrant in classification systems, it is required to publish and disseminate various Korean food recipes and publications to add new headings or notes in future classification systems.

• Journal of the Korean Society for information Management
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• v.18 no.1
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• pp.7-22
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• 2001

The DO1 is an opaque string or dumb number - nothing at all can inferred from the number. However some identifies can include some intelligence in the number itself, which will tell the user subject and meaningful information about the entity that is being identified. Therefore whether the DO1 suffix should be 'dumb' or 'meaningful' is a major issue in DO1 syntax structuring. Although the choice of DO1 Suffix is entirely optional, many DO1 registrants have been used existing IS0 standards such as SICI, ISBN, ISMN, etc. for identification of information resources. It is also important to consider that intelligence in a system of identifiers may be carried out by means of the DO1 structure. It was suggested that there should be an explicit part of the DO1 syntax which declares the meaningful information of the digital objects.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.307-327
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• 2023

To analyze the impact of word embedding on book titles, this study utilized word embedding models (Word2vec, GloVe, fastText) to generate embedding vectors from book titles. These vectors were then used as classification features for automatic classification. The classifier utilized the k-nearest neighbors (kNN) algorithm, with the categories for automatic classification based on the DDC (Dewey Decimal Classification) main class 300 assigned by libraries to books. In the automatic classification experiment applying word embeddings to book titles, the Skip-gram architectures of Word2vec and fastText showed better results in the automatic classification performance of the kNN classifier compared to the TF-IDF features. In the optimization of various hyperparameters across the three models, the Skip-gram architecture of the fastText model demonstrated overall good performance. Specifically, better performance was observed when using hierarchical softmax and larger embedding dimensions as hyperparameters in this model. From a performance perspective, fastText can generate embeddings for substrings or subwords using the n-gram method, which has been shown to increase recall. The Skip-gram architecture of the Word2vec model generally showed good performance at low dimensions(size 300) and with small sizes of negative sampling (3 or 5).