Reconciling OWL and non-monotonic rules for the semantic web

Matthias Knorr, Pascal Hitzler, Frederick Maier

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

We propose a description logic extending SROIQ (the description logic underlying OWL 2 DL) and at the same time encompassing some of the most prominent monotonic and nonmonotonic rule languages, in particular Datalog extended with the answer set semantics. Our proposal could be considered a substantial contribution towards fulfilling the quest for a unifying logic for the Semantic Web. As a case in point, two non-monotonic extensions of description logics considered to be of distinct expressiveness until now are covered in our proposal. In contrast to earlier such proposals, our language has the "look and feel" of a description logic and avoids hybrid or first-order syntaxes.

Original languageEnglish
Title of host publicationECAI 2012
EditorsLuc de Raedt, Christian Bessiere, Didier Dubois, et al
PublisherIOS
Pages474-479
Number of pages6
ISBN (Electronic)978-1-61499-098-7
ISBN (Print)978-1-61499-097-0
DOIs
Publication statusPublished - 1 Dec 2012
Event20th European Conference on Artificial Intelligence - Montpellier, France
Duration: 27 Aug 201231 Aug 2012

Publication series

NameFrontiers in artificial intelligence and applications
PublisherIOS Press
Volume242
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference20th European Conference on Artificial Intelligence
Abbreviated titleECAI 2010
CountryFrance
CityMontpellier
Period27/08/1231/08/12
OtherIncluding Prestigious Applications of Artificial Intelligence (PAIS-2012)

Bibliographical note

This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License.

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    Knorr, M., Hitzler, P., & Maier, F. (2012). Reconciling OWL and non-monotonic rules for the semantic web. In L. de Raedt, C. Bessiere, D. Dubois, & et al (Eds.), ECAI 2012 (pp. 474-479). (Frontiers in artificial intelligence and applications; Vol. 242). IOS. https://doi.org/10.3233/978-1-61499-098-7-474