• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.3
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• pp.150-170
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• 2008

In recent years, main focus of research on image retrieval techniques is on content-based image retrieval. Text-based image retrieval schemes, on the other hand, provide semantic support and efficient retrieval of matching images. In this paper, based on Formal Concept Analysis (FCA), we propose a new image indexing and retrieval technique. The proposed scheme uses keywords and textual annotations and provides semantic support with fast retrieval of images. Retrieval efficiency in this scheme is independent of the number of images in the database and depends only on the number of attributes. This scheme provides dynamic support for addition of new images in the database and can be adopted to find images with any number of matching attributes.

• Journal of Information Technology Services
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• v.7 no.3
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• pp.271-286
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• 2008

The core of the semantic web is ontology, which supports interoperability among semantic web applications and enables developer to reuse and share domain knowledge. It used a variety of fields such as Information Retrieval, E-commerce, Software Engineering, Artificial Intelligence and Bio-informatics. However, the reality is that various errors might be included in conceptual hierarchy when developing ontologies. Therefore, methodologies and supporting tools are essential to help the developer construct suitable ontologies for the given purposes and to detect and analyze errors in order to verify the inconsistency in the ontologies. In this paper we propose a new approach for ontology error detection based on the Concept Lattices of Formal Concept Analysis. By using the tool that we developed in this research, we can extract core elements from the source code of Ontology and then detect some structural errors based on the concept lattices. The results of this research can be helpful for ontology engineers to support error detection and construction of "well-defined" and "good" ontologies.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.149-161
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• 2013

In this paper, we propose a novel approach to construction of classification hierarchies for user tags of folksonomies, using WordNet-based Formal Concept Analysis tool, called TagLighter, which is developed on this research. Finally, to give evidence of the usefulness of this approach in practice, we describe some experiments on user tag data of Bibsonomy.org site. The classification hierarchies of user tags constructed by our approach allow us to gain a better and further understanding and insight in tagged data during information retrieval and data analysis on the folksonomy-based systems. We expect that the proposed approach can be used in the fields of web data mining for folksonomy-based web services, social networking systems and semantic web applications.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.479-493
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• 2010

Along with the transformation of the knowledge-based environment, e-learning has become a main teaching and learning method, prompting various research efforts to be conducted in this field. One major research area in e-learning involves adaptive learning systems that provide personalized learning content according to each learner's characteristics by taking into consideration a variety of learning circumstances. Active research on ontology-based adaptive learning systems has recently been conducted to provide more efficient and adaptive learning content. In this paper, we design and propose an adaptive learning system based on the concept lattice of Formal Concept Analysis (FCA) with the same objectives as those of ontology approaches. However, we are in pursuit of a system that is suitable for learning of specific domains and one that allows users to more freely and easily build their own adaptive learning systems. The proposed system automatically classifies the learning objects and concepts of an evolved domain in the structure of a concept lattice based on the relationships between the objects and concepts. In addition, the system adaptively constructs and presents the learning structure of the concept lattice according to each student's level of knowledge, learning style, learning preference and the learning state of each concept.

• School Mathematics
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• v.14 no.4
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• pp.469-493
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• 2012

The purpose of this study is find the relation between students' concept and types of proof construction. For this, four undergraduate students majored in mathematics education were evaluated to examine how they understand mathematical concepts and apply their concepts to their proving. Investigating students' proof with their concepts would be important to find implications for how students have to understand formal concepts to success in proving. The participants' proof productions were classified into syntactic proof productions and semantic proof productions. By comparing syntactic provers and semantic provers, we could reveal that the approaches to find idea for proof were different for two groups. The syntactic provers utilized procedural knowledges which had been accumulated from their proving experiences. On the other hand, the semantic provers made use of their concept images to understand why the given statements were true and to get a key idea for proof during this process. The distinctions of approaches to proving between two groups were related to students' concepts. Both two types of provers had accurate formal concepts. But the syntactic provers also knew how they applied formal concepts in proving. On the other hand, the semantic provers had concept images which contained the details and meaning of formal concept well. So they were able to use their concept images to get an idea of proving and to express their idea in formal mathematical language. This study leads us to two suggestions for helping students prove. First, undergraduate students should develop their concept images which contain meanings and details of formal concepts in order to produce a meaningful proof. Second, formal concepts with procedural knowledge could be essential to develop informal reasoning into mathematical proof.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.8
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• pp.1877-1893
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• 2009

Folksonomy is a user-generated taxonomy to organize information by which a user assigns tags to resources published on the web. Triadic datas that indicate relations of between users, tags, and resources, are created by collaborative tagging from many users in folksonomy-based system. Such the folksonomy data has been utilized in the field of the semantic web and web2.0 as metadata about web resources. In this paper, we propose FCA-based folksonomy data mining approach in order to extract the useful information from folksonomy data with various points of view. And we developed tool for supporting our approach. In order to verify the usefulness of our proposed approach and FMT, we have done some experiments for data of del.icio.us, which is a popular folksonomy-based bookmarking system. And we report about result of our experiments.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.spc
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• pp.39-53
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• 2023

Recently, the market competition has been fiercer due to the acceleration of technological change and the launch of intelligent products. In this situation, technology cooperation activities through networks rather than independent technological innovation activities of a single company or institution are recognized as a crucial strategy to gain competitiveness. Technology cooperation can take various forms depending on the target technology, and researchers have conducted performance analyses of technology cooperation types. However, there have not been data-based quantitative studies on the types and trends of technology cooperation for the target technology. In this paper, we explored the difference between the technology cooperation types by technology and time using the formal concept analysis method and co-patent information. In particular, the proposed methodology has been verified through the case study of electric vehicles, and it is intended to suggest the direction of technological cooperation according to specific technologies and cooperation targets in the future

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1158-1167
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• 2017

Many real-world applications information are organized and represented with graph structure which is often used for representing various ubiquitous networks, such as World Wide Web, social networks, and protein-protein interactive networks. In particular, similarity evaluation between graphs is a challenging issue in many fields such as graph searching, pattern discovery, neuroscience, chemical compounds exploration and so forth. There exist some algorithms which are based on vertices or edges properties, are proposed for addressing this issue. However, these algorithms do not take both vertices and edges similarities into account. Towards this end, this paper pioneers a novel approach for similarity evaluation between graphs based on formal concept analysis. The feature of this approach is able to characterize the relationships between nodes and further reveal the similarity between graphs. Therefore, the highlight of our approach is to take vertices and edges into account simultaneously. The proposed algorithm is evaluated using a case study for validating the effectiveness of the proposed approach on detecting and measuring the similarity between graphs.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.123-129
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• 2016

This study had a purpose to find the correlation between academic achievement and entrance examination score using FCA and framing a concept of university academic achievement and entrance examination score and then, building concept lattice. The subject for this analysis was admission materials of freshmen in computer engineering department at A university located in Chungcheong area in 2010. Also 56 students in total were subjects for the score analysis of their first semester from 2010 to 2014. The major findings of this study were as follows. We organized and extracted many-valued context, concept lattice, associated rules using FCA. And we analyzed the correlation between University academic achievement and entrance examination score using FCA. The conclusions were as follows. We could get meaningful analyzed results using FCA. University academic achievement had lower relativeness with entrance examination score and female students had higher university academic achievement than male students. The improvement points are summarized below. It is necessary to improve the ratio and application method of the university admission factors and to increase the regular admission for a university. And programs for completing College-preparatory programming for male students in low university academic achievement are necessary.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.802-809
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• 2013

In this paper, we propose the similarity measurement method between two program codes by counting the frequency and length of continuous patterns of specifiers and keywords, which exist in two program codes. In addition, we propose the visualization method of this analysis result by formal concept analysis. Proposed method considers adjacencies of specifiers or keywords, which have not been considered in the previous similarity measurements. Proposed method can detect the plagiarism by analyzing the pattern in each function regardless of the order of function call and execution. In addition, the result of the similarity measurement is visualized by the lattice of formal concept analysis to increase the user understanding about the relations between program codes. Experimental results showed that proposed method succeeded in 96% plagiarism detections. Our method could be applied into the analysis of general documents.