Pavel Klinov
University of Manchester
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Publication
Featured researches published by Pavel Klinov.
european semantic web conference | 2008
Pavel Klinov
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.
international semantic web conference | 2008
Pavel Klinov; Bijan Parsia
This paper describes the first steps towards developing a methodology for testing and evaluating the performance of reasoners for the probabilistic description logic P-
international semantic web conference | 2011
Chiara Del Vescovo; Damian Gessler; Pavel Klinov; Bijan Parsia; Ulrike Sattler; Thomas Schneider; Andrew Winget
{ensuremath{mathcal{SHIQ}}(D)}
conference on automated deduction | 2011
Pavel Klinov; Bijan Parsia
. 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-
Journal of Information Technology Research | 2011
Pavel Klinov; Bijan Parsia; David Picado Muiño
{ensuremath{mathcal{SHIQ}}(D)}
international conference on logic programming | 2010
Pavel Klinov; Bijan Parsia; David Picado-Muiño
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-
scalable uncertainty management | 2009
Pavel Klinov; Bijan Parsia
{ensuremath{mathcal{SHIQ}}(D)}
international workshop description logics | 2009
Pavel Klinov; Bijan Parsia; Ulrike Sattler
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-
owl experiences and directions | 2008
Pavel Klinov; Bijan Parsia
{ensuremath{mathcal{SHIQ}}(D)}
uncertainty reasoning for the semantic web | 2011
Pavel Klinov; Bijan Parsia
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.