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Book ChapterDOI

P-SHOQ(D): A Probabilistic Extension of SHOQ(D) for Probabilistic Ontologies in the Semantic Web

TLDR
This paper presents a probabilistic extension of SHOQ(D), called P-SHOQ (D), to allow for dealing with Probabilistic ontologies in the semantic web, and presents sound and complete reasoning techniques that show in particular that reasoning in P- SHOZ(D) is decidable.
Abstract
Ontologies play a central role in the development of the semantic web, as they provide precise definitions of shared terms in web resources. One important web ontology language is DAML+OIL; it has a formal semantics and a reasoning support through a mapping to the expressive description logic SHOQ(D) with the addition of inverse roles. In this paper, we present a probabilistic extension of SHOQ(D), called P-SHOQ(D), to allow for dealing with probabilistic ontologies in the semantic web. The description logic P-SHOQ(D) is based on the notion of probabilistic lexicographic entailment from probabilistic default reasoning. It allows to express rich probabilistic knowledge about concepts and instances, as well as default knowledge about concepts. We also present sound and complete reasoning techniques for P-SHOQ(D), which are based on reductions to classical reasoning in SHOQ(D) and to linear programming, and which show in particular that reasoning in P-SHOQ(D) is decidable.

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Citations
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Journal ArticleDOI

Managing uncertainty and vagueness in description logics for the Semantic Web

TL;DR: This paper gives an overview of approaches in this context to managing probabilistic uncertainty, possibilistic Uncertainty, and vagueness in expressive description logics for the Semantic Web.
Proceedings ArticleDOI

A probabilistic extension to ontology language OWL

TL;DR: This work proposes to incorporate Bayesian networks (BN), a widely used graphic model for knowledge representation under uncertainty and OWL, the de facto industry standard ontology language recommended by W3C to support uncertain ontology representation and ontology reasoning and mapping.
Journal ArticleDOI

Expressive probabilistic description logics

TL;DR: This paper presents 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.
Journal ArticleDOI

Semantics for the Semantic Web: The Implicit, the Formal and the Powerful

TL;DR: The central message of this article is that building the Semantic Web purely on description logics will artificially limit its potential, and that it will need to both exploit well-known techniques that support implicit semantics, and develop more powerful semantic techniques.
Journal ArticleDOI

Reasoning with very expressive fuzzy description logics

TL;DR: The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy- SI and fuzzy -SHIN.
References
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Journal ArticleDOI

Probabilistic logic

TL;DR: In this paper, a semantical generalization of logic in which the truth values of sentences are probabilistic values (between 0 and 1) is presented, which applies to any logical system for which the consistency of a finite set of sentences can be established.
Book

Weaving the Web

TL;DR: This document analyze the modeling in the software area through of use of UML, too it propose the development of a tool under the actual known philosophy called web 2.0.
Journal ArticleDOI

OIL: an ontology infrastructure for the Semantic Web

TL;DR: The authors present OIL, a proposal for a joint standard for specifying and exchanging ontologies, which is needed for knowledge sharing and reuse on the Semantic Web.
Book ChapterDOI

Practical Reasoning for Expressive Description Logics

TL;DR: An algorithm is presented that decides satisfiability of the DL ACC extended with transitive eind inverse roles, role hierarchies, and quaJifying number restrictions, and early experiments indicate that this algorithm is well-suited for implementation.
Journal ArticleDOI

Reasoning within fuzzy description logics

TL;DR: This paper presents a fuzzy extension of ALC, combining Zadeh's fuzzy logic with a classical DL, where concepts becomes fuzzy and, thus, reasoning about imprecise concepts is supported.
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