%0 Journal Article %1 Lukasiewicz2008Expressive %A Lukasiewicz, Thomas %D 2008 %J Artificial Intelligence %K description ecai2010-maxdlsat logics probabilistic %N 6-7 %P 852 - 883 %R DOI: 10.1016/j.artint.2007.10.017 %T Expressive probabilistic description logics %U http://www.sciencedirect.com/science/article/B6TYF-4R1MF4C-1/2/36132fd965af5a169b53a197616f4721 %V 172 %X The work in this paper is directed towards sophisticated formalisms for reasoning under probabilistic uncertainty in ontologies in the Semantic Web. Ontologies play a central role in the development of the Semantic Web, since they provide a precise definition of shared terms in web resources. They are expressed in the standardized web ontology language OWL, which consists of the three increasingly expressive sublanguages OWL Lite, OWL DL, and OWL Full. The sublanguages OWL Lite and OWL DL have a formal semantics and a reasoning support through a mapping to the expressive description logics and , respectively. In this paper, we present the expressive probabilistic description logics P- and P-, which are probabilistic extensions of these description logics. They allow for expressing rich terminological probabilistic knowledge about concepts and roles as well as assertional probabilistic knowledge about instances of concepts and roles. They are semantically based on the notion of probabilistic lexicographic entailment from probabilistic default reasoning, which naturally interprets this terminological and assertional probabilistic knowledge as knowledge about random and concrete instances, respectively. As an important additional feature, they also allow for expressing terminological default knowledge, which is semantically interpreted as in Lehmann's lexicographic entailment in default reasoning from conditional knowledge bases. Another important feature of this extension of and by probabilistic uncertainty is that it can be applied to other classical description logics as well. We then present sound and complete algorithms for the main reasoning problems in the new probabilistic description logics, which are based on reductions to reasoning in their classical counterparts, and to solving linear optimization problems. In particular, this shows the important result that reasoning in the new probabilistic description logics is decidable/computable. Furthermore, we also analyze the computational complexity of the main reasoning problems in the new probabilistic description logics in the general as well as restricted cases.

@article{Lukasiewicz2008Expressive, abstract = {The work in this paper is directed towards sophisticated formalisms for reasoning under probabilistic uncertainty in ontologies in the Semantic Web. Ontologies play a central role in the development of the Semantic Web, since they provide a precise definition of shared terms in web resources. They are expressed in the standardized web ontology language OWL, which consists of the three increasingly expressive sublanguages OWL Lite, OWL DL, and OWL Full. The sublanguages OWL Lite and OWL DL have a formal semantics and a reasoning support through a mapping to the expressive description logics and , respectively. In this paper, we present the expressive probabilistic description logics P- and P-, which are probabilistic extensions of these description logics. They allow for expressing rich terminological probabilistic knowledge about concepts and roles as well as assertional probabilistic knowledge about instances of concepts and roles. They are semantically based on the notion of probabilistic lexicographic entailment from probabilistic default reasoning, which naturally interprets this terminological and assertional probabilistic knowledge as knowledge about random and concrete instances, respectively. As an important additional feature, they also allow for expressing terminological default knowledge, which is semantically interpreted as in Lehmann's lexicographic entailment in default reasoning from conditional knowledge bases. Another important feature of this extension of and by probabilistic uncertainty is that it can be applied to other classical description logics as well. We then present sound and complete algorithms for the main reasoning problems in the new probabilistic description logics, which are based on reductions to reasoning in their classical counterparts, and to solving linear optimization problems. In particular, this shows the important result that reasoning in the new probabilistic description logics is decidable/computable. Furthermore, we also analyze the computational complexity of the main reasoning problems in the new probabilistic description logics in the general as well as restricted cases.}, added-at = {2010-02-16T10:34:10.000+0100}, author = {Lukasiewicz, Thomas}, biburl = {https://www.bibsonomy.org/bibtex/2b5d41d90482656b13148561f8664028c/casi}, description = {ScienceDirect - Artificial Intelligence : Expressive probabilistic description logics}, doi = {DOI: 10.1016/j.artint.2007.10.017}, interhash = {e42b0f99c1566cc709807445cf586668}, intrahash = {b5d41d90482656b13148561f8664028c}, issn = {0004-3702}, journal = {Artificial Intelligence}, keywords = {description ecai2010-maxdlsat logics probabilistic}, number = {6-7}, pages = {852 - 883}, timestamp = {2010-02-16T10:34:10.000+0100}, title = {Expressive probabilistic description logics}, url = {http://www.sciencedirect.com/science/article/B6TYF-4R1MF4C-1/2/36132fd965af5a169b53a197616f4721}, volume = 172, year = 2008 }