• The Journal of Korean Association of Computer Education
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• v.12 no.6
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• pp.53-67
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• 2009

In this study we surveyed the characteristics of the Semantic Web and ontology technology, analyzing the studies which applied ontology to e-Learning. In addition, we investigated the models which should be considered in the adaptive learning, analyzing the existing adaptive learning systems. On the basis of the analysis of them, we sought the ways to apply ontology for supporting the adaptive learning in the e-learning system, designing an ontology-based adaptive learning system. The system made up for the weak points of the existing ontology-based learning systems. That is, it appropriately diagnoses learners' knowledge level of learning concepts, classifying the learning styles in detail, and providing their corresponding learning methods and content. By adapting the learning content to the learners' individual learning style and knowledge level, this system would support their learning more efficiently and more effectively.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.754-759
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• 2007

Methodology of ontology building based on Web resources will not only reduce significantly the ontology construction period, but also enhance the quality of the ontology. Remarkable progress has been achieved in this regard, but they encounter similar difficulties, such as the Web data extraction and knowledge acquisition. This paper researches on the characteristics of ontology construction data, including dynamics, largeness, variation and openness and other features, and the fundamental issue of ontology construction - formalized representation method. Then, the key technologies used in and the difficulties with ontology construction are summarized. A software Model-OntoMaker (Ontology Maker) is designed. The model is innovative in two regards: (1) the improvement of generality: the meta learning machine will dynamically pick appropriate ontology learning methodologies for data of different domains, thus optimizing the results; (2) the merged processing of (semi-) structural and non-structural data. In addition, as known to all wetland researchers, information sharing is vital to wetland exploitation and protection, while wetland ontology construction is the basic task for information sharing. OntoMaker constructs the wetland ontologies, and the model in this work can also be referred to other environmental domains.

• The Journal of Korean Association of Computer Education
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• v.13 no.5
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• pp.29-38
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• 2010

In this study we developed an ontology-based e-learning system for supporting self-directed learning. In this system, a domain ontology of a learning topic was constructed and relation properties were defined to indicate the relations among the learning concepts. The learning concepts and their relationships are structured visually through the domain ontology. It also boosts understandabilities of students by means of the visualization of relationships among the pre and post concepts. In addition, the system provides reasoning so that learners can do intelligent query when they want to learn more or they are curious about the high-level knowledge while they are learning a topic. These features of the system would help learners' self-directed and active learning.

• International Journal of Internet, Broadcasting and Communication
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• v.4 no.2
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• pp.7-11
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• 2012

In this paper, we defined constructivism and ontology theory and associate it in ubiquitous learning. The typical ubiquitous learning involving the Context Aware Intelligent system was presented. Also the Architecture for learning environment including the key idea and technical concept is being presented in this paper. Guided with these principles and with the advancement of information and communication technology the context-awareness based on Artificial intelligence for Location based Service for ubiquitous Learning was conceptualized.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.97-105
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• 2016

With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.

• Journal of Engineering Education Research
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• v.11 no.2
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• pp.15-24
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• 2008

Teaching is communication between instructor and students. The learning outcomes can be enhanced by active learning of students. However, there are many obstacles to effective learning below, such as lecture notes authored by instructor, passive student participation, and paper-based homework. In this paper, we propose an effective method for enhancing learning effect through constructing learner ontologies in which knowledge discovered by students is conceptualized and organized. The learning ontology is composed of a teacher ontology and many learner ontologies. The learning ontology is used in discussion, visual presentation, and knowledge sharing between instructor and students. We used the learning ontology in two lectures in practice and learned that the learning ontology enhances learning effect through analysis of feedbacks of students.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.157-162
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• 2008

This paper presents a learning method of tagging ontology using large tagging data such as a folksonomy, which stands for classification structure informally created by the people. There is no common agreement about the semantics of a tagging, and most social web sites internally use different methods to represent tagging information, obstructing interoperability between sites and the automated processing by software agents. To solve this problem, we need a tagging ontology, defined by analyzing intrinsic attributes of a tagging. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tagging ontology is also suggested as an applying field.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.2
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• pp.389-394
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• 2011

The object of this paper is to make learners have study environment to study adaptively, anywhere, anyone, anytime and, just in time, not disturbed by time and place. So, it helps learners find solutions to questions and problems which they can face in the process of learning. This paper desires to find possibility of ontology which can solve problems after considering semantic web and theory of ontology by studying existing reference books. As ontology has the structure that can guess the data which is not showed clearly, so it can make the result more accurate and be the knowledge every learner sympathize and trust. I established the ontology frame about the electronic circuit which learners can solve their questions everywhere, anytime, and reconfirm what they studied, so I studied on application for e-learning based on ontology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.645-646
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• 2018

Recently, the concept of machine learning has been introduced as a decision making method through data processing. Machine learning uses the results of running based on existing data as a means of decision making. The data generated by the development of technology is vast. This data is called big data. It is important to extract the necessary data from these data. In this paper, we propose a method for extracting related data for constructing an ontology through machine learning. The results of machine learning can be given a relationship from a semantic perspective. it can be added to the ontology to support relationships depending on the needs of the application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.993-996
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• 2009

The object of this study is to make leaners have studying environment to study adaptively, any where, any one, any time, and just in time. So, it helps leaners find solutions to questions and problems which they can face in the process of learning. This study tried to find a solution to possibility of ontologied electronic circuit, after consideration of the Semantic web and ontology theory through studying of Sundry records. As the result, I established the ontology frame about the electronic circuit, and I studied on application for e-learning based on the Semantic web ontology.