• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.27-44
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• 2008

Owl-based ontology is useful to realize the context-aware services which are composed of the distributed and self-configuring modules. Many ontology-based inference engines are developed to infer useful information from ontology. Since these engines show the uniqueness in terms of speed and information richness, it's difficult to ensure stable operation in providing dynamic context-aware services, especially when they should deal with the complex and big-size ontology. To provide a best inference service, the purpose of this paper is to propose a novel methodology of context-aware engine selection in a contextually prompt manner Case-based reasoning is applied to identify the causality between context and inference engined to be selected. Finally, a series of experiments is performed with a novel evaluation methodology to what extent the methodology works better than competitive methods on an actual context-aware service.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.296-301
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• 2012

Currently, in order to develop an intelligent search engine to help users retrieve information effectively, many metodes have been used. The effective retrieval methods of these methods use ontology technology. Ontology technology is the core of the Semantic Web. In the Semantic Web, ontology technology can be used to retrieve related information through the inference engine more accurately and simply on the Semantic Web. In this paper, we implement and design college information retrieval based on ontology to retrieve college class, graduate school class and person class. We have collected the hierarchy structure about the College, graduate school and person informations, and we have used protege editor of the ontology developing tool to design some ontologies with the College informations collected. We also tested the designed ontology with the Inference Engine(Pellet) of protege editor, and implemented college information retrieval system using Inference Engine(Jena) for web services.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.45-52
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• 2009

Recently, we started a project development of the digital expert system for the product design supporting in manufacturing industry. This digital expert system is used to the engineers in manufacturing industry for the process control, production management and system management. In this paper, we develop the ontology based inference engine shell for building of expert system. This expert system shell included a various functions which of Korean language supporting, graphical ontology map modeling interface, fuzzy rule definition function and etc. And, we introduce the knowledge representation method for the ontology map building and ontology based fuzzy inferencing method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.235-240
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• 2004

In this paper, we propose a framework for ontological knowledge-based image understanding systems. Ontology composed of concepts can be used as a guide for describing objects from a specific domain of interest and describing relations between objects from different domains The proposed framework consists of four main subparts ⅰ) ontological knowledge bases, ⅱ) primitive feature detectors, ⅲ) concept inference engine, and ⅳ) semantic inference engine. Using ontological knowledge bases on various domains and features extracted from the detectors, concept inference engine infers concepts on regions of interest in an image and semantic inference engine reasons semantic situations between concepts from different domains. We present a outline for ontological knowledge-based image understanding systems and application examples within specific domains such as text recognition and human recognition in order to show the validity of the proposed system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2016-2034
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• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.875-880
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• 2005

OWL(Web Ontology Language) is the Ontology Standard Language and the a lot of Ontologies are being constructed in OWL. But the research on the extension of OWL is also progressing because of the limit of representation power of in OWL language. The W3C suggests the SWRL(Semantic Web Rule Language) based on the combination of OWL and RuleML(Rule Markup Language), which is improved in the representation of rule. Thus, both OWL and SWRL are used for developing ontologies. However, research on inference of ontologies written in both languages is just begun. These day, for the inference of ontologies written in both languages, ontologies and divided in to two parts. The part written in OWL and written in SWRL. For the inference of the part written in OWL, Racer, a DL based inference engine, is used and for the other part Jess, a rule-based engine, is used. In this paper, we will propose three methods for integrated inference of the OWL part and the SWRL part of ontologies using Jess and some tools for ontology inference : OWLJessKB and SWRL Factory

• ETRI Journal
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• v.41 no.4
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• pp.515-527
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• 2019

This paper discusses the design, implementation, and potential use of an ontology-based mobile pediatric consultation and monitoring system, which is a smart healthcare expert system for pediatric patients. The proposed system provides remote consultation and monitoring of pediatric patients during their illness at places distant from medical service areas. The system not only shares instant medical data with a pediatrician but also examines the data as a smart medical assistant to detect any emergency situation. In addition, it uses an inference engine to infer instant suggestions for performing certain initial medical treatment steps when necessary. The applied methodologies and main technical contributions have three aspects: (a) pediatric consultation and monitoring ontology, (b) semantic Web rule knowledge base, and (c) inference engine. Two case studies with real pediatric patients are provided and discussed. The reported results of the applied case studies are promising, and they demonstrate the applicability, effectiveness, and efficiency of the proposed approach.

• Journal of KIISE
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• v.43 no.1
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• pp.61-70
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• 2016

In recent years, there has been a need for techniques for large-scale ontology inference in order to infer new knowledge from existing knowledge at a high speed, and for a diversity of semantic services. With the recent advances in distributed computing, developments of ontology inference engines have mostly been studied based on Hadoop or Spark frameworks on large clusters. Parallel programming techniques using GPGPU, which utilizes many cores when compared with CPU, is also used for ontology inference. In this paper, by combining the advantages of both techniques, we propose a new method for reasoning large RDFS ontology data using a Spark in-memory framework and inferencing distributed data at a high speed using GPGPU. Using GPGPU, ontology reasoning over high-capacity data can be performed as a low cost with higher efficiency over conventional inference methods. In addition, we show that GPGPU can reduce the data workload on each node through the Spark cluster. In order to evaluate our approach, we used LUBM ranging from 10 to 120. Our experimental results showed that our proposed reasoning engine performs 7 times faster than a conventional approach which uses a Spark in-memory inference engine.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.67-81
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• 2006

While the existing Web focuses on the interface with human users based on HTML, the next generation Web will focus on the interaction among software agents by using XML and XML-based standards and technologies. The inference engine, which will serve as brains of software agents in the next generation Web, should thoroughly understand the Semantic Web, the standard language of the next generation Web. As abasis for the service, the W3C (World Wide Web Consortium) has recommended SWRL (Semantic Web Rule Language) which had been made by compounding OWL (Web Ontology Language) and RuleML (Rule Markup Language). In this research, we develop a backward chaining inference engine SMART-B (SeMantic web Agent Reasoning Tools -Backward chaining inference engine), which uses SWRL and OWL to represent rules and facts respectively. We analyze the requirements for the SWRL-based backward chaining inference and design analgorithm for the backward chaining inference which reflects the traditional backward chaining inference algorithm and the requirements of the next generation Semantic Web. We also implement the backward chaining inference engine and the administrative tools for fact and rule bases into Java components to insure the independence and portability among different platforms under the environment of Ubiquitous Computing.

• Journal of the Korean Society for Library and Information Science
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• v.49 no.3
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• pp.81-107
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• 2015

This study describes the Bottom-up method for implementation of an ontology system from the RDB. The STNet, a structured terminology glossary based on RDB, was served as a test bed for converting to RDF ontology, for generating the inference rules, and for evaluating the results of the semantic search. We have used protege editor of the ontology developing tool to design ontologies with test data. We also tested the designed ontology with the Inference Engine (Pellet) of protege editor. The generated reference rules were tested by TBox and SPARQL queries through STNet ontology. The results of test show that the generated reference rules were verified as true and STNet ontology were also evaluated to be useful for searching the complex combination of semantic relation.