Although PR-OWL is focused in the Semantic Web, it tackles a problem that precedes even the current WWW: the quest for more efficient data exchange. Clearly, solving that problem requires more precise semantics and flexible ways to convey information. While the WWW provided a new presentation medium and technologies such as XML presented new data exchange formats, both failed to address the semantics of data being exchanged. The SW is meant to fill this gap, and the realization of its goals will require major improvements in technologies for data exchange.
Unfortunately, for historical reasons and due to the lack of expressivity of probabilistic representations in the past, current ontology languages have no built-in support for representing or reasoning with uncertain, incomplete information. In the uncertainty-laden environment in which the SW will operate, this is a major shortcoming preventing realization of the SW vision. Indeed, in almost any domain represented in the SW there will exist a vast body of knowledge that would be completely ignored (i. e. neither represented nor reasoned upon) due to the SW language’s inability to deal with it.
As a means of addressing this problem, the long-term goal of PR-OWL is to establish a Bayesian framework for probabilistic ontologies, which will provide a basis for plausible reasoning services in the Semantic Web. Clearly, the level of acceptance and standardization required for achieving this objective requires a broader effort led by the W3C, probably resulting in a W3C Recommendation formally extending the OWL language. Thus, the present status of research on PR-OWL should be seen as an initial effort towards that broader objective.
To get a broader understanding of the scenario in which PR-OWL is expected to be used, the prospective reader might want to see brief introductory overviews of Web languages, probability theory, and Bayesian Networks. This will provide the necessary context information to understand a brief coverage on the attempts to find a common ground between the SW and probabilistic representations.
Understanding PR-OWL at a deeper level requires some background on Multi-Entity Bayesian Networks (MEBN), the probabilistic first-order logic that is the mathematical backbone of PR-OWL. As a means to provide a smooth introduction to the fairly complex concepts of MEBN logic, we wanted to explore a domain of knowledge that would be both easily understood and politically neutral, while still rich enough to include scenarios that would demand a highly expressive language. Thus, we constructed a running case study based on the Star Trek™ (1) television series. Our explanations and examples assume no previous familiarity with the particulars of the Star Trek™ series.
Another important issue is the very definition of a probabilistic ontology, a key concept for understanding a language that was written as an upper probabilistic ontology.