Probabilistic Ontology: Representation and Modeling Methodology

Abstract

The past few years have witnessed an increasingly mature body of research on the Semantic Web (SW), with new standards being developed and more complex problems being addressed. As complexity increases in SW applications, so does the need for principled means to cope with uncertainty in SW applications. Several approaches addressing uncertainty representation and reasoning in the SW have emerged. Among these is Probabilistic Web Ontology Language (PR-OWL), which provides WEB Ontology Language(OWL) constructs for representing Multi-Entity Bayesian network (MEBN) theories. However, there are several important ways in which the initial version PR-OWL 1 fails to achieve full compatibility with OWL. Furthermore, although there is an emerging literature on ontology engineering, little guidance is available on the construction of probabilistic ontologies. This research proposes a new syntax and semantics, defined as PR-OWL 2, which improves compatibility between PR-OWL and OWL in two important respects. First, PR-OWL 2 follows the approach suggested by Poole et al. to formalizing the association between random variables from probabilistic theories with the individuals, classes and properties from ontological languages such as OWL. Second, PR-OWL 2 allows values of random variables to range over OWL datatypes. To address the lack of support for probabilistic ontology engineering, this research describes a new methodology for modeling probabilistic ontologies called the Uncertainty Modeling Process for Semantic Technologies (UMP-ST). To better explain the methodology and to verify that it can be applied to different scenarios, this dissertation presents step-by-step constructions of two different probabilistic ontologies. One is used for identifying frauds in public procurements in Brazil and the other is used for identifying terrorist threats in the maritime domain. Both use cases demonstrate the advantages of PR-OWL 2 over its predecessor.