A MAUT APPROACH FOR REUSING DOMAIN ONTOLOGIES ON THE BASIS OF THENeOnMETHODOLOGY

A. Jiménez (), M. C. Suárez-Figueroa (), A. Mateos (), A. Gómez-Pérez () and M. Fernández-López ()
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A. Jiménez: Decision Analysis and Statistics Group, Dept. de Inteligencia Artificial, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid 28660, Spain
M. C. Suárez-Figueroa: Ontology Engineering Group, Dept. de Inteligencia Artificial, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid 28660, Spain
A. Mateos: Decision Analysis and Statistics Group, Dept. de Inteligencia Artificial, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid 28660, Spain
A. Gómez-Pérez: Ontology Engineering Group, Dept. de Inteligencia Artificial, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid 28660, Spain
M. Fernández-López: Escuela Politécnica Superior, Universidad San Pablo CEU, Boadilla del Monte, Madrid 28660, Spain

International Journal of Information Technology & Decision Making (IJITDM), 2013, vol. 12, issue 05, 945-968

Abstract: Knowledge resource reuse is becoming a widespread approach in the ontology engineering field because it can speed up the ontology development process. In this context, theNeOn Methodologyspecifies some guidelines for reusing different types of knowledge resources (ontologies, nonontological resources, and ontology design patterns). These guidelines prescribe how to perform the different activities involved in any of the diverse types of reuse processes. One such activity is to select the best knowledge resources for reuse in an ontology development. This selection activity is a complex decision-making problem involving conflicting objectives, like understandability, integration or reliability. We propose a multi-attribute utility theory (MAUT) approach to deal with the selection of the best domain ontologies for reuse, stressing the identification of attributes to measure ontology performances. We take advantage of the sensitivity analysis tools provided by the GMAA system, a PC-based decision support system based on an additive multi-attribute utility model, to exploit imprecise information on the inputs. An example concerning the selection of a subset of ontologies for reuse in the development of a new ontology in the sports domain illustrates the approach.

Keywords: Ontology engineering; reuse; multi-attribute utility theory; decision support system; 68T30; 91B06; 91B16 (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1142/S021962201340004X

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