Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/20033
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dc.contributor.advisorBell, D-
dc.contributor.advisorSerrano-Rico, A-
dc.contributor.authorMd. Saleh, Nurul Izrin-
dc.date.accessioned2020-01-17T11:56:00Z-
dc.date.available2020-01-17T11:56:00Z-
dc.date.issued2019-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/20033-
dc.descriptionThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University Londonen_US
dc.description.abstractThe proliferation and ubiquity of SemanticWeb technologies have transformed the way computer society reshapes its technology through knowledge integration, knowledge reuse and knowledge sharing. Ontology, one of the Semantic Web components, is a way to represent domain knowledge into a human-understandable and machine-readable format. Ontology in simulation has been seen as a conceptual model of a system in an explicit and unambiguous manner, where it can be applied to better capture the modeler’s perspective of the domain. Regarding an ontology for simulation modeling, by reusing ontologies, it helps to reduce time and effort in attaining the domain knowledge, and at the same time assist in domain understanding. For a semantically-richer simulation ontology, it is useful to engage with real data and existing ontologies. This research contributes a rigorous method that extracts domain knowledge, synthesizes processes performed within the domain, and builds a minimal and viable ontology for simulation modeling, knownas aMinimal Viable Simulation Ontology (MVSimO). The research method initially applies ontology selection techniques in Ontology Reuse Framework (ORF) to obtain suitable existing ontologies for reuse. ORF incorporates a module extraction technique during the domain conceptualization phase, where the modules will represent domain knowledge as sub-ontologies. Formal Concept Analysis is later applied to the real-world data to reveal the process details of the domain. Finally, the development of MVSimO is completed by the derivation of event semantic of the processes. The effectiveness of ontology selection and synthesizing methods, is reviewed by evaluating the selected ontology knowledge extracted, and the detailed ontological model of MVSimO. The evaluation of,MVSimO is performed to determine its agreement to the established simulation model of the domain. The evaluation results are encouraging, providing concrete outcomes of the new technique of ontology reuse and new development to the research area.en_US
dc.language.isoenen_US
dc.publisherBrunel University Londonen_US
dc.relation.urihttps://bura.brunel.ac.uk/retrieve/85472/FulltextThesis.pdf-
dc.subjectHealthcareen_US
dc.subjectSemantic weben_US
dc.subjectOntologyen_US
dc.subjectFormal concept analysisen_US
dc.subjectSimulation modellingen_US
dc.titleOntology reuse and synthesis for modelling and simulationen_US
dc.typeThesisen_US
Appears in Collections:Computer Science
Dept of Computer Science Theses

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