Probabilistic Ontology and Knowledge Fusion for Procurement Fraud Detection in Brazil

Presented at: Uncertainty Reasoning for the Semantic Web (URSW2009)

by Rommel Carvalho, Kathryn B. Laskey, Paulo C. G. Costa, Marcelo Ladeira, Laécio Santos, Shou Matsumoto

Webpage: http://CEUR-WS.org/Vol-527/paper1.pdf

To cope with society’s demand for transparency and corruption prevention, the Brazilian Federal General Comptroller (CGU) has carried out a number of actions, including: awareness campaigns aimed at the private sector; campaigns to educate the public; research initiatives; and regular inspections and audits of municipalities and states. Although CGU has collected information from hundreds of different sources - Revenue Agency, Federal Police, and others - the process of fusing all this data has not been efficient enough to meet the needs of CGU’s decision makers. Therefore, it is natural to change the focus from data fusion to knowledge fusion. As a consequence, traditional syntactic methods must be augmented with techniques that represent and reason with the semantics of databases. However, commonly used approaches fail to deal with uncertainty, a dominant characteristic in corruption prevention. This paper presents the use of Probabilistic OWL (PR-OWL) to design and test a model that performs information fusion to detect possible frauds in procurements involving Federal money. To design this model, a recently developed tool for creating PR-OWL ontologies was used with support from PR-OWL specialists and careful guidance from a fraud detection specialist from CGU.

Keywords: MEBN, PR-OWL, UnBBayes, knowledge fusion, ontology, probabilistic ontology, procurement fraud detection


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