• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.151-173
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• 2005

We developed a methodology of rule acquisition from texts such as Web pages which utilizes ontology in identification of rule components. We expect that the proposed methodology can reduce the bottleneck of rule acquisition and contribute to the utilization of rule based systems. As parts of our research, we designed an ontology for rule acquisition named OntoRule and proposed a rule acquisition methodology through OntoXRML which is an acquisition tool using OntoRule. Also, we evaluated our approach by calculating missed recommendations and wrong recommendations of rule components in rule acquisition experiments over three online bookstores.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.191-198
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• 2017

It is needed to support intelligent customized health information service for user convenience in PHR based Personal Health Care Service Platform. In this paper, we specify an ontology-based health data model for Personal Health Care Service Platform. We also design a knowledge search algorithm that can be used to figure out similar health record by applying machine learning and data mining techniques. Axis-based mining algorithm, which we proposed, can be performed based on axis-attributes in order to improve relevance of knowledge exploration and to provide efficient search time by reducing the size of candidate item set. And K-Nearest Neighbor algorithm is used to perform to do grouping users byaccording to the similarity of the user profile. These algorithms improves the efficiency of customized information exploration according to the user 's disease and health condition. It can be useful to apply the proposed algorithm to a process of inference in the Personal Health Care Service Platform and makes it possible to recommend customized health information to the user. It is useful for people to manage smart health care in aging society.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.600-608
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• 2020

Machine learning constructs an objective function from learning data, and predicts the result of the data generated by checking the objective function through test data. In machine learning, input data is subjected to a normalisation process through a preprocessing. In the case of numerical data, normalization is standardized by using the average and standard deviation of the input data. In the case of nominal data, which is non-numerical data, it is converted into a one-hot code form. However, this preprocessing alone cannot solve the problem. For this reason, we propose a method that uses ontology to normalize input data in this paper. The test data for this uses the received signal strength indicator (RSSI) value of the Wi-Fi device collected from the mobile device. These data are solved through ontology because they includes noise and heterogeneous problems.

• The Journal of the Korea Contents Association
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• v.5 no.2
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• pp.29-37
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• 2005

Korean cyber universities now provide higher education service to learners but the courses suffer major limitations of low quality and inefficient format of their learning contents. Because of the absence of any standard authoring strategy for learning contents, tutors develop their own courseware with various formats and content structures. In addition, the lack of association between content learners causes them difficulties in finding and reusing related contents. In this paper, we propose an authoring strategy foradvanced learning contents based on SCORM and ontology. Our strategy improvesthe reusability and associativity of learning contents. We demonstrate the effectiveness of our proposed authoring strategy through developing learning contents such as understanding of digital contents.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.545-554
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• 2006

Although the current Web search engines provide tremendous information, it is hard to find right information among the huge information. Users need to spend extra time to filter out unnecessary information. In order to overcome the limit of current Web search engines, Semantic Web was developed to provide efficient search, integration, and reuse of information by structuring semantic information from Web resources. In this paper, an elementary education contents retrieval system using Semantic Web is proposed. The proposed system emphasizes history contents that have high relevancy among data. For construction of the proposed system, ontology is proposed first for elementary study contents and ontology for historical contents is proposed for easy access to those contents using semantic relation among them. Based on the ontology, the proposed system is designed and implemented. The proposed system has the following characteristics. First, the system provides various query formats in detail so that search results can be refined efficiently. Second, the system presents only semantically information connected with key words or including key words using study contents ontology. Finally, the proposed system can increase study effects by presenting various contents that are related with query by users.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06c
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• pp.168-171
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• 2011

One important issue in semantic web is identification and selection of domain concepts for domain ontology learning when several hundreds or even thousands of terms are extracted and available from relevant text documents shared among the members of a domain. We present a novel domain concept acquisition and selection approach for ontology learning that uses affinity propagation algorithm, which takes as input semantic and structural similarity between pairs of extracted terms called data points. Real-valued messages are passed between data points (terms) until high quality set of exemplars (concepts) and cluster iteratively emerges. All exemplars will be considered as domain concepts for learning domain ontologies. Our empirical results show that our approach achieves high precision and recall in selection of domain concepts using less number of iterations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.187-190
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• 2016

본 연구는 국내 교육과정의 빈번한 변경으로 인해 교사나 학생들이 교육이나 학습에 어려움을 겪고 있는 문제를 해결하기 위해 교육과정이나 교육 단계 등을 온톨로지를 기반으로 자동 연계되어 교육과정이나 체계에 상관없이 교육내용을 접근할 수 있는 방안을 제공한다. 초등.중등.고등학교 간 연계되어야 할 교육내용이 교육과정 변경에 따라 표현되는 상이한 교육과정체계로 인해 다양한 문제를 야기하고 사회적. 기술적 어려움을 가중시키는 것을 완화하고자 본 방법을 제안한다. 본 방법은 교과과정에서 교육 내용의 중심이 되는 성취기준을 기본으로 교육 단원-교육내용 등을 연계하고 또 교육 내용에 따라 제공되는 교육 콘텐츠까지도 연계함으로서 교사나 학생등이 쉽게 교육 내용을 접근하고 사용할 수 있는 장점이 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.487-489
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• 2005

본 논문에서는 온톨로지의 개념구조를 이용한 웹페이지의 의미적 분류방법을 제안한다. 웹 문서들이 가지는 용어 정보들과 어휘들 간의 개념 구조를 파악하여 온톨로지를 확장시키면서 이를 문서분류에 적용하여 의미적 분류가 이루어지게 한다. 문서 분류는 문서들을 가장 잘 표현할 수 있는 자질들을 정하고 이러한 자질들을 통해 미리 정의된 2개 이상의 카테고리에 문서의 내용을 파악하여 가장 관련이 있는 카테고리로 할당하는 것이다. 본 논문에서는 웹 문서에서 추출한 용어 정보들의 유사도와 온톨로지 카테고리의 유사도를 계산하여 웹 문서를 분류하여 문서 분류를 위한 실험데이터나 학습과정 없이 바로 실시간으로 문서분류가 이루어지며, 결과적으로 온톨로지와 문서들이 가지는 고유한 의미와 관계의 식별을 통하여 보다 더 정확하게 문서분류를 가능하게 해준다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.2
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• pp.182-187
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• 2006

This paper proposes a method for boundary recognition of named entity using hidden markov model and ontology information of biological named entity. We uses smoothing method using 31 feature information of word and hierarchical information to alleviate sparse data problem in HMM. The GENIA corpus version 2.1 was used to train and to experiment the proposed boundary recognition system. The experimental results show that the proposed system outperform the previous system which did not use ontology information of hierarchical information and smoothing technique. Also the system shows improvement of execution time of boundary recognition.