• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.34-39
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• 2021

Collaborative ontology construction is the latest trend in developing ontologies. In this technique domain specialists and ontologists need to work together. Because of the complexity associated with ontology construction, it's done in an iterative and incremental fashion. After each iteration, an ontology increment will be produced. Current ontology increment is always an enhanced version of the previous increment. Each ontology increment has to be verified for its accuracy. Domain specialists' contribution is very significant in accomplishing this necessity. Unfortunately, non-computing domain specialists (i.e. medical doctors, bankers, lawyers) are illiterate on semantic concepts. Therefore, validating the accuracy of the ontology increment is a complex hurdle for them. This research proposes verbalization approach to address this complexity.

• Journal of the Korea Society for Simulation
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• v.18 no.1
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• pp.27-34
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• 2009

Currently network users, especially the number of internet users, increase rapidly. Also, high quality of service is required and this requirement results a sudden network traffic increment. As a result, an efficient management system for huge network traffic becomes an important issue. Ontology/data engineering based context awareness using the System Entity Structure (SES) concepts enables network administrators to access traffic data easily and efficiently. The network traffic analysis system, which is studied in this paper, is designed and implemented based on a model and simulation using data engineering methodology to be avaiable in evaluating large network traffic data. Extensible Markup Language (XML) is used for metadata language in this system. The information which is extracted from the network traffic analysis system could be modeled and simulated in Discrete Event Simulation (DEVS) methodology for further works such as post simulation evaluation, web services, and etc.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.19-30
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• 2012

The context aware service is the service to provide useful information to the users by recognizing surroundings around people who receive the service via computer based on computing and communication, and by conducting self-decision. But CAS(Context Awareness System) shows the weak point of small-scale context awareness processing capacity due to restricted mobile function under the current mobile environment, memory space, and inference cost increment. In this paper, we propose a mobile cloud context system with using Google App Engine based on PaaS(Platform as a Service) in order to get context service in various mobile devices without any subordination to any specific platform. Inference design method of the proposed system makes use of knowledge-based framework with semantic inference that is presented by SWRL rule and OWL ontology and Jess with rule-based inference engine. As well as, it is intended to shorten the context service reasoning time with mapping the regular reasoning of SWRL to Jess reasoning engine by connecting the values such as Class, Property and Individual which are regular information in the form of SWRL to Jess reasoning engine via JessTab plug-in in order to overcome the demerit of queries reasoning method of SparQL in semantic search which is a previous reasoning method.