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Synthetic Environment Representational Semantics Using the Web Ontology Language
Department of Computer Science, La Trobe University, Melbourne, Australia. (AASS)ORCID iD: 0000-0002-6290-5492
Department of Computer Science, La Trobe University, Melbourne, Australia.
Air-Operations Division, DSTO, Melbourne, Australia.
2005 (English)In: Intelligent Data Engineering and Automated Learning - IDEAL 2005: 6th International Conference, Brisbane, Australia, July 6-8, 2005, Proceedings / [ed] Marcus Gallagher, James P. Hogan, Frederic Maire, Springer , 2005, Vol. 3578, p. 9-16Conference paper, Published paper (Refereed)
Abstract [en]

The application of Ontologies for the definition and interoperability of complementary taxonomies has been well-recognised within the Modelling & Simulation (M&S) community. Our research pertaining to the specification of Synthetic Environment (SE) representational semantics has proposed the use of an Synthetic Environment Data Representation Ontology (sedOnto), which is modeled using W3C ' s Web Ontology Language(OWL), The vocabulary specified in sedOnto is based the SEDRIS Data Representation Model (DRM), which is a technological framework for SE data interchange and interoperability. In this paper, we present STOWL - SEDRIS To OWL Transform that automates the transformation of a SEDRIS based SE to a Web-Ontology based representation scheme in the OWL language. The target representation scheme, which shall be based on sedOnto, is in actuality an instantiation of the SE data representation terminology as specified by sedOnto. Such a transformation has many perceived advantages: It enhances SE interoperability by utilizing a Web-Ontology based approach for the specification of SE representation data, is consistent with existing industry based SE representation standards, namely SEDRIS, and that the representation scheme facilitates ontological reasoning over SE objects; a facility that is not directly supported by the SEDRIS DRM.

Place, publisher, year, edition, pages
Springer , 2005. Vol. 3578, p. 9-16
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 3578
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-64277DOI: 10.1007/11508069_2ISI: 000230878800002Scopus ID: 2-s2.0-26444452002ISBN: 978-3-540-26972-4 (print)ISBN: 978-3-540-31693-0 (electronic)OAI: oai:DiVA.org:oru-64277DiVA, id: diva2:1174445
Conference
6th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2005), Brisbane, Australia, July 6-8, 2005
Available from: 2018-01-15 Created: 2018-01-15 Last updated: 2018-01-22Bibliographically approved

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Bhatt, Mehul

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CiteExportLink to record
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Citation style
  • apa
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  • Other locale
More languages
Output format
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