Enterprise architecture reference modeling in OWL/RDF

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Abstract

This paper describes the design of and the deployment options for the Federal Enterprise Architecture Reference Model Ontology (FEA-RMO). The goal of any reference model is to provide a basis or starting point for some design process. While this is a laudable goal, it poses an immediate problem for representation; how can a model be represented in such a way that it can be extended in certain ways (for application to a particular problem), but not without regard to the advice that it gives? Reference models are usually expressed in natural language. At their best, such models provide a starting point for designers, and a checklist for their designs, to see that they conform to industry best practices. At worst, reference models expressed in natural language become a source of busy work; designers do not use the models during the design process, instead they spend time after the fact writing up an explanation of how and why they are compliant with the reference framework they've never seriously considered. In this paper, we have used Semantic Web technologies (in particular, RDF and OWL) to represent a reference mode for enterprise architecture in the US government. The content of the model comes from the recent Federal Enterprise Architecture Reference Model effort. We use the capability of RDF to distribute structured information to allow the reference model to be extended (as intended in its design). We use OWL to maintain the consistency of those extensions. The model has been used as the basis for an implementation of an FEA registry, a web-based system for managing enterprise architectures based on the FEA. The work of representing the FEA as formal ontologies was funded in part by GSA. 1 © Springer-Verlag Berlin Heidelberg 2005.

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CITATION STYLE

APA

Allemang, D., Polikoff, I., & Hodgson, R. (2005). Enterprise architecture reference modeling in OWL/RDF. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3729 LNCS, pp. 844–857). https://doi.org/10.1007/11574620_60

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