Abstract
The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model can be used to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.
Highlights
The ability to represent and reason with uncertainty is important across a wide range of domains
This paper describes the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a methodology for defining a probabilistic ontology and using it for plausible reasoning in applications that use semantic technology
The use case was presented with a focus on illustrating the activities that must be executed within each discipline in the Probabilistic Ontology Modeling Cycle (POMC) cycle in the context of the fraud identification problem
Summary
The ability to represent and reason with uncertainty is important across a wide range of domains. This paper describes the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a methodology for defining a probabilistic ontology and using it for plausible reasoning in applications that use semantic technology. The methodology is illustrated through a use case in which semantic technology is applied to the problem of identifying fraud in public procurement in Brazil. The purpose of the use case is to show how to apply the methodology on a simplified but realistic problem, and to provide practical guidance to probabilistic ontology designers on how to apply the UMP-ST. Our focus is primarily on the procurement fraud use case, but the UMP-ST is applicable to any domain in which semantic technology can be applied. The paper concludes with a section on future work and a final section presenting our concluding remarks
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