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Repurposing the Resource-Event-Agent Enterprise Ontology Through Formalization

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  • F. GAILLY

    ()

  • G. GEERTS
  • G. POELS

Abstract

Applications such as business modelling, model-driven software development, active use of knowledge specifications and enterprise application integration benefit from formal ontology specifications. In this paper we create two formal representations of an enterprise ontology, the Resource-Event-Agent Enterprise Ontology (REA-EO), aiming at repurposing its use to such more advanced applications. The first formal specification is graphical in nature and has as objective to more precisely capture REA-EO’s semantics as well as an integrated definition of an extended set of structuring rules. The second formal specification is a machine-readable version of the REA-EO defined with the OWL knowledge representation language. We use a case study to demonstrate how business modelling and enterprise application integration benefit from these formal specifications.

Suggested Citation

  • F. Gailly & G. Geerts & G. Poels, 2010. "Repurposing the Resource-Event-Agent Enterprise Ontology Through Formalization," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/659, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:10/659
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    File URL: http://wps-feb.ugent.be/Papers/wp_10_659.pdf
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    References listed on IDEAS

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    1. Wagner Kamakura & Carl Mela & Asim Ansari & Anand Bodapati & Pete Fader & Raghuram Iyengar & Prasad Naik & Scott Neslin & Baohong Sun & Peter Verhoef & Michel Wedel & Ron Wilcox, 2005. "Choice Models and Customer Relationship Management," Marketing Letters, Springer, vol. 16(3), pages 279-291, December.
    2. P. Baecke & D. Van Den Poel, 2009. "Data Augmentation by Predicting Spending Pleasure Using Commercially Available External Data," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/596, Ghent University, Faculty of Economics and Business Administration.
    3. Thomas J. Steenburgh & Andrew Ainslie & Peder Hans Engebretson, 2003. "Massively Categorical Variables: Revealing the Information in Zip Codes," Marketing Science, INFORMS, vol. 22(1), pages 40-57, August.
    4. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
    5. W. Buckinx & E. Moons & D. Van Den Poel & G. Wets, 2003. "Customer-Adapted Coupon Targeting Using Feature Selection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/201, Ghent University, Faculty of Economics and Business Administration.
    6. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
    7. A. Prinzie & D. Van Den Poel, 2007. "Random Forrests for Multiclass classification: Random Multinomial Logit," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/435, Ghent University, Faculty of Economics and Business Administration.
    8. Levy, Ori & Galili, Itai, 2008. "Stock purchase and the weather: Individual differences," Journal of Economic Behavior & Organization, Elsevier, vol. 67(3-4), pages 755-767, September.
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