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fulltext (1) - Ontology Engineering The DOGMA Approach...

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T.S. Dillon et al. (Eds.): Advances in Web Semantics I, LNCS 4891, pp. 7–34, 2008. © IFIP International Federation for Information Processing 2008 Ontology Engineering – The DOGMA Approach Mustafa Jarrar and Robert Meersman STARLab, Vrije Universiteit Brussel, Belgium {mjarrar,meersman}@vub.ac.be Abstract. This chapter presents a methodological framework for ontology en- gineering (called DOGMA), which is aimed to guide ontology builders towards building ontologies that are both highly reusable and usable, easier to build and to maintain. We survey the main foundational challenges in ontology engineer- ing and analyse to what extent one can build an ontology independently of ap- plication requirements at hand. We discuss ontology reusability verses ontology usability and present the DOGMA approach, its philosophy and formalization, which prescribe that an ontology be built as separate domain axiomatization and application axiomatizations. While a domain axiomatization focuses on the characterization of the intended meaning (i.e. intended models) of a vocabulary at the domain level, application axiomatizations focus on the usability of this vocabulary according to certain application/usability perspectives and specify the legal models (a subset of the intended models) of the application(s)’ interest. We show how specification languages (such as ORM, UML, EER, and OWL) can be effectively (re)used in ontology engineering. 1 Introduction and Motivation The Internet and other open connectivity environments create a strong demand for sharing the semantics of data. Ontologies are becoming increasingly essential for nearly all computer science applications. Organizations are looking towards them as vital machine-processable semantic resources for many application areas. An ontol- ogy is an agreed understanding (i.e. semantics) of a certain domain, axiomatized and represented formally as logical theory in the form of a computer-based resource. By sharing an ontology, autonomous and distributed applications can meaningfully com- municate to exchange data and thus make transactions interoperate independently of their internal technologies. Research on ontologies has turned into an interdisciplinary subject. It combines elements of Philosophy, Linguistics, Logics, and Computer Science. Within computer science, the research on ontologies emerged “mainly” within two subcommunities: ar- tificial intelligence (among scientists largely committed to building shared knowledge bases) and database (among scientists and members of industry who are largely com- mitted to building conceptual data schemes, also called semantic data models [V82]). This legacy in computer science brings indeed successful techniques and methods to enrich the art of ontology engineering. However, some confusion on how to reuse these techniques is witnessed. For example, many researchers have confused ontolo- gies with data schemes, knowledge bases, or even logic programs.
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8 M. Jarrar and R. Meersman
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