• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.145-156
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• 1996

Although knowledge discovery is increasingly important in databases, the discovered knowledge sets may not be effectively used for application domains. It is partly because knowledge discovery does not take user's interests into account, and too many knowledge sets are discovered to handle efficiently. We believe that user's interests are conveyed by a query and if a nested query is concerned it may include a user's thought process. This paper describes a novel concept for discovering knowledge sets based on query processing. Knowledge discovery process is performed by: extracting features from databases, spanning features to generate range features, and constituting a knowledge set. The contributions of this paper include the following: (1) not only simple queries but also nested queries are considered to discover knowledge sets regarding user's interests and user's thought process, (2) not only positive examples (answer to a query) but also negative examples are considered to discover knowledge sets regarding database abstraction and database exceptions, and (3) finally, the discovered knowledge sets are quantified.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.8
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• pp.281-286
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• 2018

With recent increase in complexity and variety of information and massively available information, interest in and necessity of ontology has been on the rise as a method of extracting a meaningful search result from massive data. Although there have been proposed many methods of extracting the ontology from a given text of a natural language, the extraction based on most of the current methods is not consistent with the structure of the ontology. In this paper, we propose a method of automatically creating ontology by distinguishing a term needed for establishing the ontology from a text given in a specific domain and extracting various relationships between the terms based on the pattern-based method. To extract the relationship between the terms, there is proposed a method of reducing the size of a searching space by taking a matching set of patterns into account and connecting a join-set concept and a pattern array. The result is that this method reduces the size of the search space by 50-95% without removing any useful patterns from the search space.

• Journal of KIISE:Software and Applications
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• v.32 no.4
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• pp.300-311
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• 2005

Terms are linguistic realization of technical concepts. Term constituents are important elements used for representing the concept. Since many new terms are created from the modification or combination of existing constituents, it is important to analyze term constituents for understanding the concept of the term. It means that term constituents offer clues for understanding the concept of terms. However, there are a couple of difficulties in matching concept unit and term constituents such as mismatching between a term constituent and a concept unit, homonym of term constituents and synonym of term constituents. To solve them, it is necessary to recognize concept units of term constituents. In this paper, we define an English term constituent as the concept unit and use an alignment algorithm between English-Korean term constituents in order to recognize concept units of term constituents. By our alignment algorithm we recognize Korean term constituents corresponding to an English term constituent with about $93\%$ precision.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.997-1008
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• 2006

For extracting and processing information explicitly from given data, Formal Concept Analysis(FCA) is provided a method which is widely used for data analysis and clustering. The data can be structured into concepts, which are formal abstractions human thought allowing meaningful comprehensible interpretation. However, most FCA tools mainly focus on analyzing one-valued contexts that represent objects, attributes and binary relations between them. There we few FCA tools available that provide scaling and analyzing many-valued contexts representing objects, attributes and relations with attributes' values. In this paper, we propose not only a scaling algorithm for interpreting and simplifying the multivalued input data, but also an algorithm to generate concepts and build concept hierarchy from given raw data as well. Based on these algorithms, we develop an automate tool, FCA Wizard, for concept analysis and concept hierarchy. We also present FCA Wizard based applications in medical domain.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.2
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• pp.147-154
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• 1999

A new approach to conducting the vibration parameters identification algorithm is proposed. The approach employs the concept of modal amplitude ratio implemented in a mode shape estimation. The accuracy of the improved Ibrahim Time Domain identification algorithm in extracting structural modal parameters from free response functions has been studied using computer simulated data for 9 stations on the two-span continuous beam. Simulated responses from the lumped and distributed parameter system demonstrate that this algorithm produces excellent results, even in the 300% noise response.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.181-186
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• 2017

The system optimization is a technique that changes the structure of the program in order to extract the duplicated modules without changing the source code, reuse of the extracted module. Structure-oriented development and object-oriented development are efficient at crosscutting concern modular, however can't be modular of crosscutting concept. To apply the crosscutting concept in an existing system, there is a need to a extracting technique for distributed system optimization module within the system. This paper proposes a method for extracting the redundant modules in the completed system. The proposed method extracts elements that overlap over a source code analysis to analyze the data dependency and control dependency. The extracted redundant element is used to program dependency analysis for the system optimization. Duplicated dependency analysis result is converted into a control flow graph, it is possible to produce a minimum crosscutting module. The element extracted by dependency analysis proposes a system optimization method which minimizes the duplicated code within system by setting the crosscutting concern module.

• Journal of the Korea Society of Computer and Information
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• v.17 no.1
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• pp.19-30
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• 2012

Based on the internet-based infrastructures such as various information devices, social network systems and cloud computing environments, distributed and sharable data are growing explosively. Recently, as a data analysis and mining technique for extracting, analyzing and classifying the inherent and useful knowledge and information, Formal Concept Analysis on binary or many-valued data has been successfully applied in many diverse fields. However, in formal concept analysis, there has been little research conducted on analyzing interval data whose attributes have some interval values. In this paper, we propose a new approach for classification of interval data based on the formal concept analysis. We present the development of a supporting tool(iFCA) that provides the proposed approach for the binarization of interval data table, concept extraction and construction of concept hierarchies. Finally, with some experiments over real-world data sets, we demonstrate that our approach provides some useful and effective ways for analyzing and mining interval data.

• Journal of The Korean Association of Information Education
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• v.14 no.4
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• pp.537-545
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• 2010

The purpose of this paper is to develop mathematics learning contents for elementary school 3rd graders and to verify the educational effectiveness of contents developed. An ADDIE model was applied to develop mathematics learning contents based on storytelling for concept learning. After extracting 54 concepts from the mathematics curriculum, researchers designed strategies using concepts that were combined with context which is familiar to young students. Researchers implemented a survey and interview to students and teachers to verify the effectiveness of contents. As a result, the understanding, interest, concentration, and expectation of students toward the contents developed were very high, and teachers also mentioned that these contents could be very useful teaching materials for motivation.

• Korean Institute of Interior Design Journal
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• v.15 no.6 s.59
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• pp.238-246
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• 2006

Recent trends of interior design have a focus on creation of more various meanings rather than past ideology which sought after the compatibility to the function of modem design. These trends requires integral understanding of social and cultural ideologies with a sens of values for a certain periods. In addition, they also require creativity which able to read, find and solve consumer's diverse demand and desire. Considering the effort of trend forecasting in Korea is still heavily rely on the foreign trend shows, it is natural to attempt to study the analytical forecasting methodology based upon more systematic principles which lead to more objective outcome, when the understanding, forcasting and analysis of interior decoration trend are required. In this thesis, the analysis and forecasting of interior decoration trend are studied by means of verbal image code process which involves the induction of design concept through data extraction, classification and analysis, in order to understanding and satisfying the diversified consumer's demand and trend. The coding process of verbal image is understanding as general concept. by extracting common elements from abstract and individual image, and/or specific concept. Therefore, it is proposed that the database building and data mining process of verbal Image, and subsequent development of programming skill can be applied as more efficient tool for various verbal image process.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.443-450
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• 2005

In this paper, we propose an intelligent model lot extraction of the web user's preference and present the results of evaluation. For this purpose, we analyze shortcomings of current information retrieval engine being used and reflect preference weights on learner. As it doesn't depend on frequency of each word but intelligently learns patterns of user behavior, the mechanism Provides the appropriate set of results about user's questions. Then, we propose the concept of preference trend and its considerations and present an algorithm for extracting preference with examples. Also, we design an intelligent model for extraction of behavior patterns and propose HTML index and process of intelligent learning for preference decision. Finally, we validate the proposed model by comparing estimated results(after applying the Preference) of document ranking measurement.