2013, Vol.47, No.1

Journal of the Korean Society for Library and Information Science 2023 KCI Impact Factor : 0.84

pISSN : 1225-598X / eISSN : 2982-6292
https://journal.kci.go.kr/kslis

The purpose of this study is to develop a faceted classification that enables the conceptualization and the organization of Korean food information. 23,470 terms were collected, categorized, and analyzed from the table of contents in 776 monographs and directory headings in portal sites related to Korean food. In order to develop the Korean food classification, common properties were analyzed in the process of categorizing terms. Then basic facets and subfacets were defined and assigned, and hierarchies among facets and concepts, citation orders, and notations were decided. As a result, the classification scheme consisted of 16 basic facets and 85 subfacets. The citation order of facets was proposed in order of Personality facet (kinds of dishes), Matter facet (materials, cooking utensils/equipment/containers, and nutrients), Energy facet (cooking processes and techniques, eating sense, type of cooking, table services, and agents of cooking), Space facet (countries/ethnic groups/geography, and eating places), Time facet (situation/purposes, season, time of meals, periods, and ages). The result of this study will be used for organizing, searching, retrieving, and providing Korean food information effectively around the world. Also, it will provide a foundation for developing subject-oriented classification using facet analysis in other disciplines.

It is a difficult problem to use keyword retrieval for low-quality Korean document images because these include adjacent characters that are connected. In addition, images that are created from various fonts are likely to be distorted during acquisition. In this paper, we propose and test a keyword retrieval system, using a support vector machine (SVM) for the retrieval of low-quality Korean document images. We propose a keyword retrieval method using an SVM to discriminate the similarity between two word images. We demonstrated that the proposed keyword retrieval method is more effective than the accumulated Optical Character Recognition (OCR)-based searching method. Moreover, using the SVM is better than Bayesian decision or artificial neural network for determining the similarity of two images.

The aim of this study is to reveal the categorizational and classificatory features of the subject-based classification (SBC) as a subject organization system. For this purpose, 12 SBC schemes of public libraries were selected and a comparative analysis was made between the traditional classification system, such as DDC and SBC in terms of the categorizational aspects, and canons for the classification. As a result, there were significant and considerable differences between the two types of classifications. This study concluded that SBC cannot be clearly explained and understood without a consideration of its essential and distinctive characteristics as a classification scheme.