Detalhes do Documento

Local closed world reasoning with description logics under the well-founded semantics

Autor(es): Knorr, Matthias ; Alferes, José Júlio ; Hitzler, Pascal

Data: 2011

Origem: Repositório Institucional da UNL

Projeto/bolsa: info:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBD%2F28745%2F2006/PT;

Assunto(s): Description logics and ontologies; Knowledge representation; Logic programming; Non-monotonic reasoning; Semantic Web; Language and Linguistics; Linguistics and Language; Artificial Intelligence


Descrição

We thank the reviewers of a previously submitted version of this paper for their very helpful comments and suggestions for improvement. We thank Frederick Maier for a thorough proofreading. Matthias Knorr acknowledges support by Fundação para a Ciência e a Tecnologia under the grant SFRH/BD/28745/2006. Pascal Hitzler acknowledges support by the National Science Foundation under award 1017225 “III: Small: TROn – Tractable Reasoning with Ontologies”.

An important question for the upcoming Semantic Web is how to best combine open world ontology languages, such as the OWL-based ones, with closed world rule-based languages. One of the most mature proposals for this combination is known as hybrid MKNF knowledge bases (Motik and Rosati, 2010 [52]), and it is based on an adaptation of the Stable Model Semantics to knowledge bases consisting of ontology axioms and rules. In this paper we propose a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs. Moreover, our proposal allows for the detection of inconsistencies, possibly occurring in tightly integrated ontology axioms and rules, with only little additional effort. We also identify tractable fragments of the resulting language.

Tipo de Documento Artigo científico
Idioma Inglês
Contribuidor(es) DI - Departamento de Informática; CENTRIA – Centro de Inteligência Artificial; RUN
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