Abstract:
Uncertainty is ubiquitous. Any representation scheme intended to model real-world actions and processes must be able to cope with the effects of uncertain phenomena.
A major shortcoming of existing Semantic Web technologies is their inability to represent and reason about uncertainty in a sound and principled manner. This not only hinders the realization of the original vision for the Semantic Web (Berners-Lee & Fischetti, 2000), but also raises an unnecessary barrier to the development of new, powerful features for general knowledge applications.
The overall goal of our research is to establish a Bayesian framework for probabilistic ontologies, providing a basis for plausible reasoning services in the Semantic Web. As an initial effort towards this broad objective, this dissertation introduces a probabilistic extension to the Web ontology language OWL, thereby creating a crucial enabling technology for the development of probabilistic ontologies.
The extended language, PR-OWL (pronounced as “prowl”), adds new definitions to current OWL while retaining backward compatibility with its base language. Thus, OWL-built legacy ontologies will be able to interoperate with newly developed probabilistic ontologies. PR-OWL moves beyond deterministic classical logic (Frege, 1879; Peirce, 1885), having its formal semantics based on MEBN probabilistic logic (Laskey, 2005).
By providing a means of modeling uncertainty in ontologies, PR-OWL will serve as a supporting tool for many applications that can benefit from probabilistic inference within an ontology language, thus representing an important step toward the World Wide Web Consortium’s (W3C) vision for the Semantic Web.
In addition, PR-OWL will be suitable for a broad range of applications, which includes improvements to current ontology solutions (i.e. by providing proper support for modeling uncertain phenomena) and much-improved versions of probabilistic expert systems currently in use in a variety of domains (e.g. medical, intelligence, military, etc).
