Pavel Klinov

Pronto: a non-monotonic probabilistic description logic reasoner

The demonstration presents Pronto - a prototype of a nonmonotonic probabilistic reasoner for very expressive Description Logics. Pronto is built on top of the OWL DL reasoner Pellet, and is capable of performing default probabilistic reasoning in the Semantic Web. It can handle uncertainty in terminological and assertional DL axioms. The demonstration covers Prontos features and capabilities as well as current challenges and limitations. It describes how an involved realistic problem of breast cancer risk assessment can be formalized in terms of probabilistic reasoning in Pronto. As an important outcome, it is anticipated that attendees should learn and better understand the potential of ontology based approaches to modeling problems involving reasoning under uncertainty.

Optimization and Evaluation of Reasoning in Probabilistic Description Logic: Towards a Systematic Approach

This paper describes the first steps towards developing a methodology for testing and evaluating the performance of reasoners for the probabilistic description logic P-

Decomposition and modular structure of BioPortal ontologies

{ensuremath{mathcal{SHIQ}}(D)}

A hybrid method for probabilistic satisfiability

. Since it is a new formalism for handling uncertainty in DL ontologies, no such methodology has been proposed. There are no sufficiently large probabilistic ontologies to be used as test suites. In addition, since the reasoning services in P-

The Consistency of the Medical Expert System CADIAG-2: A Probabilistic Approach

{ensuremath{mathcal{SHIQ}}(D)}

The consistency of the CADIAG-2 knowledge base: a probabilistic approach

are mostly query oriented, there is no single problem (like classification or realization in classical DL) that could be an obvious candidate for benchmarking. All these issues make it hard to evaluate the performance of reasoners, reveal the complexity bottlenecks and assess the value of optimization strategies. This paper addresses these important problems by making the following contributions: First, it describes a probabilistic ontology that has been developed for the real-life domain of breast cancer which poses significant challenges for the state-of-art P-

On Improving the Scalability of Checking Satisfiability in Probabilistic Description Logics

{ensuremath{mathcal{SHIQ}}(D)}

On Correspondences between Probabilistic First-Order and Description Logics

reasoners. Second, it explains a systematic approach to generating a series of probabilistic reasoning problems that enable evaluation of the reasoning performance and shed light on what makes reasoning in P-

Probabilistic Modeling and OWL: A User Oriented Introduction to P-SHIQ(D)

{ensuremath{mathcal{SHIQ}}(D)}

Representing sampling distributions in P- SROIQ

hard in practice. Finally, the paper presents an optimized algorithm for the non-monotonic entailment. Its positive impact on performance is demonstrated using our evaluation methodology.