• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.265-273
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• 2012

It is essential to have Context-aware technology for personalization recommendation services and the appropriate representation and definition of Context information for context-aware. Ontology is possible to represent knowledge freely and knowledge can be extended by inferring. In addition, design of the ontology model is needed according to the purposes of utilization. This paper used context-aware technologies to implement a user personalization recommendation service. It also proposed the context through OWL modeling for user personalization recommendation service and used inference rules and inference engine for context reasoning.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.96-101
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• 2010

The core study of semantic web is the efficiency of ontology parsing. The ontology parsing and inference is based on the significant information retrieval which is the ultimate purpose of semantic web. However, most existing ontology writing tools were not processing the efficient ontology parsing. Therefore, we design the two steps ontology parser for extracting the all facts, are included in the ontology, more fast in this study. In the first step, the token extractor collects the all tokens of ontology and the triple extractor extracts the statements in the collected tokens. In conclusion, we confirm that which is designed in this study, processes the ontology parsing more faster than the existing ontology parsers.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.4
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• pp.254-263
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• 2006

Ontology-based context models are widely used in ubiquitous computing environment because they have advantages in the acquisition of conceptual context through inferencing, context sharing, and context reusing. Among the benefits, inferencing enables context-aware applications to use conceptual contexts which cannot be acquired by sensors. However, inferencing causes processing delay and thus becomes the major obstacle to the implementation of context-aware applications. The delay becomes longer as the amount of contexts increases. In this paper, we propose a context pre-fetching method to reduce the size of contexts to be processed in a working memory in attempt to speed up inferencing. For this, we extend the query-tree method to identify contexts relevant to the queries of a context-aware application. Maintaining the pre-fetched contexts optimal in a working memory, the processing delay of inference reduces without the loss of the benefits of ontology-based context model. We apply the proposed scheme to our ubiquitous computing middleware, Active Surroundings, and demonstrate the performance enhancement by experiments.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.65-67
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• 2010

위치기반서비스(LBS)는 사용자의 위치를 기반으로 다양한 정보제공 서비스를 하고 있다. 최근 연구에서는 단순한 정보제공이 아닌 사용자의 상황인식(Context-Aware)을 통하여 사용자에게 적합한 정보를 제공해주는 지능화된 서비스를 제공하고 있다. 하지만 현재 연구들은 사용자의 기본정보와 선호도정보를 이용한 단일기준 추론을 통하여 사용자에게 정보를 제공해주고 있으며, 이것은 사용자의 다양한 기준의 의사결정을 반영하지 못하는 한계점이 있다. 이러한 한계점을 극복하기 위하여 본 연구에서는 사용자의 정보, 선호도, 공간지리선호도 정보 Ontology를 구축하고, 의사 결정 기준에 가중치를 부여하는 Cost Value Ontology를 구축하여, 다 기준 의사추론을 통해 사용자에게 적절한 추천 결과가 도출되는 Ontology 추론시스템을 제안한다. 사용자들의 개인적인 특성 지식과 공간지리 선호도 지식을 구축할 수 있으며, 이러한 특성으로 구축된 지식 기반 하에 입력된 사용자 정보와 추론을 통하여 이 시스템을 통해 사용자의 선호도 Ontology를 구축할 수 있으며 이를 이용한 추론을 통하여 사용자의 현재상황에 적합한 결과를 도출함을 보였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• The Journal of Korean Medical History
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• v.25 no.2
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• pp.105-112
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• 2012

The purpose of this paper is to suggest diagnosis support system for diseases of mind so that users can make effective decisions without professional knowledge by developing efficient knowledge system and utilizing ontology with which questions and logic inference are possible to diagnose diseases of mind. Furthermore, this diagnosis support system could be applied to supplement previous diagnosis method which depends on experiences by activating the diagnose of mind diseases thru ontology and determining state of mind effectively without technical knowledge. As a result of this experiment, diagnosis support system for diseases of mind was found to be accordance with the result of consulting instructions and show additional relevance thru utility extension.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.314-317
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• 2006

This study proposes an ontology design framework to support the cooperation among devices by using PROLOG, Conceptual Graph (CG), and Resource Description Framework (RDF). Quite a large number of representation languages for representing ontology on the Web have been established over the last decade. Most of these researches are focused on design of independent resources description. In Semantic Web, however, cooperation ontology will be needed. In this study, the CG could make an entire conceptual view of knowledge and RDF can represent that knowledge. Then the PROLOG could support the natural inference based on that knowledge. Therefore, our proposed ontology will be used in the designing of Semantic Web-based cooperation systems.

• Journal of KIISE
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• v.43 no.5
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• pp.553-561
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• 2016

Several machine learning techniques are able to automatically populate ontology data from web sources. Also the interest for large scale ontology reasoning is increasing. However, there is a problem leading to the speculative result to imply uncertainties. Hence, there is a need to consider the reliability problems of various data obtained from the web. Currently, large scale ontology reasoning methods based on the trust value is required because the inference-based reliability of quantitative ontology is insufficient. In this study, we proposed a large scale OWL Horst reasoning method based on a confidence value using spark, a distributed in-memory framework. It describes a method for integrating the confidence value of duplicated data. In addition, it explains a distributed parallel heuristic algorithm to solve the problem of degrading the performance of the inference. In order to evaluate the performance of reasoning methods based on the confidence value, the experiment was conducted using LUBM3000. The experiment results showed that our approach could perform reasoning twice faster than existing reasoning systems like WebPIE.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.2
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• pp.49-63
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• 2008

RDF is the base ontology model which is used in Semantic Web defined by W3C. OWL expands the RDF base model by providing various vocabularies for defining much more ontology relationships. Recently Jain and Farkas have suggested an RDF access control model based on RDF triple. Their research point is to introduce an authorization conflict problem by RDF inference which must be considered in RDF ontology data. Due to the problem, we cannot adopt XML access control model for RDF, although RDF is represented by XML. However, Jain and Farkas did not define the authorization propagation over the RDF upper/lower ontology concepts when an RDF authorization is specified. The reason why the authorization specification should be defined clearly is that finally, the authorizatin conflict is the problem between the authorization propagation in specifying an authorization and the authorization propagation in inferencing authorizations. In this article, first we define an RDF access authorization specification based on RDF triple in detail. Next, based on the definition, we analyze the authoriztion conflict problem by RDF inference in detail. Next, we briefly introduce a method which can quickly find an authorization conflict by using graph labeling techniques. This method is especially related with the subsumption relationship based inference. Finally, we present a comparison analysis with Jain and Farkas' study, and some experimental results showing the efficiency of the suggested conflict detection method.

• Journal of Korean Library and Information Science Society
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• v.44 no.1
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• pp.385-408
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• 2013

The purpose of this study is to build the tax ontology which can be used to manage imposables by the state or local governments. In this, the tax and related concepts were analyzed and then concept hierarchy i.e., taxonomies were formed. Especially, in the concept hierarchy, after multiple inherits were decomposed as 'primitive concepts' and then Rector's 'methodology of ontology implementation normalization', in which defined concepts were recombined, was used. The methodology employed was that the tax system, which was entangled with the direct taxes, local taxes, and property taxes that has multiple-inherits, was expressed explicitly and logically. After that, automatic classification was carried out through the inference engine, consistency was verified. Finally, some practical cases of ontology created were enumerated.