Towards a Generic Fraud Ontology in e-Government

Fraud detection and prevention systems are based on various technological paradigms but the two prevailing approaches are rule-based reasoning and data mining. In this paper we claim that ontologies, an increasingly popular and widely accepted knowledge representation paradigm, can help both of these approaches be more efficient as far as fraud detection is concerned and we introduce a methodology for building domain specific fraud ontologies in the e-government domain. The main characteristic of this methodology is a generic fraud ontology that serves as a common ontological basis on which the various domain specific fraud ontologies can be built. The methodology along with the generic fraud ontology consist a powerful conceptual tool through which knowledge engineers can easily adapt ontology-based fraud detection systems to virtually any e-government domain.

[1]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[2]  M. de Rijke,et al.  Modal Logic , 2001, Cambridge Tracts in Theoretical Computer Science.

[3]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[4]  Yan Tang,et al.  Towards Ontology-based E-mail Fraud Detection , 2005, 2005 portuguese conference on artificial intelligence.

[5]  L. Goble The Blackwell guide to philosophical logic , 2001 .

[6]  Donald Nute,et al.  Defeasible Logic , 1994, INAP.

[7]  Ingrid Zukerman,et al.  # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Predictive Statistical Models for User Modeling , 1999 .

[8]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[9]  El-Bachir Belhadji,et al.  Development of an Expert System for the Automatic Detection of Automobile Insurance Fraud , 1998 .

[10]  John Kingston,et al.  Towards a Financial Fraud Ontology: A Legal Modelling Approach , 2004, Artificial Intelligence and Law.

[11]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[12]  Diego Calvanese,et al.  The description logic handbook: theory , 2003 .

[13]  James Lam Enterprise Risk Management: From Incentives to Controls , 2003 .

[14]  Dov M. Gabbay,et al.  Handbook of Logic in Artificial Intelligence and Logic Programming: Volume 3: Nonmonotonic Reasoning and Uncertain Reasoning , 1994 .

[15]  Mariano Fernández-López,et al.  Ontological Engineering , 2003, Encyclopedia of Database Systems.