• Korean Journal of Cognitive Science
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• v.11 no.1
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• pp.47-65
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• 2000

Categorization systems of homepages in search engines are likely to be constructed considering system's efficiency only but not user-centered. This study I investigated user's mental model of superordinate and subordinate categories using category terms of major Korean search engines. From this result, we constructed two kinds of categorization system; redundant system and singular system. In the redundant system, for example, a subordinate category can belong to a number of superordinate categories, but in the singular system to only one superordinate category Three prototype categorization systems, with 'Simmani', were designed and search performances of each system were observed repetitively Overall results, with frequency of correct a answers, number of steps and time taken in solution taken into account, showed the redundant system was superior to the other two systems. This indicates that categorization search could be improved with appropriate categorizaton system. However. l in recognition test score in singular system was the best, which indicates that search performance and recognition memory of categorization reveal different aspects of categorization system learning. Issues of category organization. ways of interface, prior knowledge, exploratory learning, and application areas are discussed further.

• Journal of the Korean Society of Systems Engineering
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• v.5 no.2
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• pp.17-26
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• 2009

This paper is about that case study of the Tube Transportation System that the new transportation system offering passenger and logistic service in a metropolis having plenty of the floating population or between medium-sized cities, and solving large issues like terrible traffic jams and environmental problems etc. in this region. Also it presented that elicitation results of performance parameter and the categorization system of it applying a systematic analysis methodology. By the medium of this paper, It showed that definition, case study, performance parameters, and the categorization system of parameters of a general tube transportation system before developing requirements of a specific tube transportation system. From now on, it will come in pretty handy in systems engineering of activities to establish a concept of a new tube transportation systems and develop requirements.

• Journal of the Korean Institute of Landscape Architecture
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• v.39 no.5
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• pp.76-90
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• 2011

As most landscape drawings tend to be recorded in electronic format, the need for layer standards is growing. While the categorization system of planting drawings has been progressing, landscape facility drawings are being delayed. So, the purpose of this study was to establish the categorization system of computerized landscape facility drawing documents. In the beginning of this study, it found that the layer categorization system of "The Standards of Construction CALS/EC computerized drawings v1. 1" and "The submit instructions of electronic design documents" are not suitable for the landscape facility drawings. 1,154 drawings drawn by 10 landscape architect offices were used to analyze the current layer categorization status. As a result, it found that "The Standards of Construction CALS/EC computerized drawings v1. 1" were not introduced in landscape facility drawings and 46% of layers were produced indefinitely. The new layer categorization system consisting of 15 facility items was drawn by applying the ISO construction information categorization system. The new layer categorization system is set on the basis of the legal code, landscape design standards, and design guidelines of public institutions. This new layer categorization system is expected to propagate at the landscape architect offices in the early.

• The Journal of Engineering Research
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• v.3 no.1
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• pp.47-56
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• 1998

This is a study on an ontology development for a content-based Expert System Document Categorization. The objectives of this study were to set up the concept of ontology and to find out the effect of ontology on Expert System. Based on this concept of ontology, it found noems for development of Expert System in the field of fine arts and showed Ontology hierarchical categorization.

• Journal of The Korean Association For Science Education
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• v.32 no.3
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• pp.524-531
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• 2012

Categorization is an important human function used to process different stimuli. It is also one of the most important factors affecting measurement of a person's classification ability. Explicit categorization, the representative system by which categorization ability is measured, can verbally describe the categorization rule. The purpose of this study was to develop a prediction model for categorization ability as it relates to the classification process of living organisms using fMRI. Fifty-five participants were divided into two groups: a model generation group, comprised of twenty-seven subjects, and a model verification group, made up of twenty-eight subjects. During prediction model generation, functional connectivity was used to analyze temporal correlations between brain activation regions. A classification ability quotient (CQ) was calculated to identify the verbal categorization ability distribution of each subject. Additionally, the connectivity coefficient (CC) was calculated to quantify the functional connectivity for each subject. Hence, it was possible to generate a prediction model through regression analysis based on participants' CQ and CC values. The resultant categorization ability regression model predictor was statistically significant; however, researchers proceeded to verify its predictive ability power. In order to verify the predictive power of the developed regression model, researchers used the regression model and subjects' CC values to predict CQ values for twenty-eight subjects. Correlation between the predicted CQ values and the observed CQ values was confirmed. Results of this study suggested that explicit categorization ability differs at the brain network level of individuals. Also, the finding suggested that differences in functional connectivity between individuals reflect differences in categorization ability. Last, researchers have provided a new method for predicting an individual's categorization ability by measuring brain activation.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.67-78
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• 2018

This article proposes the modified KNN (K Nearest Neighbor) algorithm which considers the feature similarity and is applied to the word categorization. The texts which are given as features for encoding words into numerical vectors are semantic related entities, rather than independent ones, and the synergy effect between the word categorization and the text categorization is expected by combining both of them with each other. In this research, we define the similarity metric between two vectors, including the feature similarity, modify the KNN algorithm by replacing the exiting similarity metric by the proposed one, and apply it to the word categorization. The proposed KNN is empirically validated as the better approach in categorizing words in news articles and opinions. The significance of this research is to improve the classification performance by utilizing the feature similarities.

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.167-167
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• 1999

The majority of text categorization systems use the term-based classification method. However, because of too many terms, this method is not effective to classify the documents in areal-time environment. This paper presents a real-time concept-based text categorization system,which classifies texts using thesaurus. The system consists of a Korean morphological analyzer, athesaurus tool, and a probability-vector similarity measurer. The thesaurus tool acquires the meaningsof input terms and represents the text with not the term-vector but the concept-vector. Because theconcept-vector consists of semantic units with the small size, it makes the system enable to analyzethe text with real-time. As representing the meanings of the text, the vector supports theconcept-based classification. The probability-vector similarity measurer decides the subject of the textby calculating the vector similarity between the input text and each subject. In the experimentalresults, we show that the proposed system can effectively analyze texts with real-time and do aconcept-based classification. Moreover, the experiment informs that we must expand the thesaurustool for the better system.

• Journal of Information Management
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• v.33 no.2
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• pp.19-32
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• 2002

With the growth of the digital library and the use of Internet, the amount of online text information has increased rapidly. The need for efficient data management and retrieval techniques has also become greater. An automatic text categorization system assigns text documents to predefined categories. The system allows to reduce the manual labor for text categorization. In order to classify text documents, the good features from the documents should be selected and the documents are indexed with the features. In this paper, each steps of text categorization and several techniques used in each step are introduced.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.2867-2875
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• 2000

In this paper, we discuss the implementation of automatic internet text categorization system. A categorization algorithm is designed and the system is implemented by back propagation learning model. Internet documents are collected according to the established categories and tested by Chi-squre ($\chi^2$) for the document leaning, and the category features are extracted. The sets of learning and separating vector are productt>d by these features. As a result of experimental evaluation, we show that this system is more improved in the performance of automatic categorization than the nearest neigbor method.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.217-226
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• 2002

In this paper, we propose a method of generating a proper categorization of morphemes by giving a hierarchical part-of-speech system and a corpus tagged using this part-of-speech system. Our method use hierarchical information in the part-of-speech system and statistical information in the corpus to generate a category set. The statistical information is based on the context of occurrence of categories. First, we specify the format of given information. Then, we describe an algorithm to generate a proper categorization. Finally, we present the results of our experiments in applying this method. We obtained a moderately proper categorization and found several candidates for improvement .