Authors:
Mauro Lopes
1
;
Giancarlo Guizzardi
2
;
Fernanda Araujo Baião
3
and
Ricardo Falbo
2
Affiliations:
1
NP2Tec – Research and Practice Group in Information Technology, Brazil
;
2
Federal University of Espírito Santo (UFES), Brazil
;
3
Federal University of the State of Rio de Janeiro (UNIRIO), Brazil
Keyword(s):
Ontology, Ontology Languages, Conceptual modelling, Oil and Gas domain.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Modeling Formalisms, Languages and Notations
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Symbolic Systems
Abstract:
Ontologies are commonly used in computer science either as a reference model to support semantic interoperability in several scenarios, or as a computer-tractable artifact that should be efficiently represented to be processed. This duality poses a tradeoff between expressivity and computational tractability that should be taken care of in different phases of an ontology engineering process. In this scenario, the choice of the ontology representation language is crucial, since different languages contain different expressivity and ontological commitments, reflecting on the specific set of available constructs. The inadequate use of a representation language, disregarding the goal of each ontology engineering phase, can lead to serious problems to database design and integration, to domain and systems requirements analysis within the software development processes, to knowledge representation and automated reasoning, and so on. This article presents an illustration of these issues by
using a fragment of an industrial case study in the domain of Oil and Gas Exploration and Production. We make explicit the differences between two different representations of this domain, and highlight a number of concepts and ideas (tacit domain knowledge) that were implicit in the original model represented using a lightweight ontology language and that became explicit by applying methodological directives underlying an ontologically well-founded modeling language.
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