Logical foundations of object-oriented and frame-based languages
Journal of the ACM (JACM)
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Combining answer set programming with description logics for the Semantic Web
Artificial Intelligence
Exploiting conjunctive queries in description logic programs
Annals of Mathematics and Artificial Intelligence
A uniform integration of higher-order reasoning and external evaluations in answer-set programming
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Answer set programming's contributions to classical logic: an analysis of ASP methodology
Logic programming, knowledge representation, and nonmonotonic reasoning
Datalog relaunched: simulation unification and value invention
Datalog'10 Proceedings of the First international conference on Datalog Reloaded
Stepwise debugging of description-logic programs
Correct Reasoning
Semantic independence in DL-programs
RR'12 Proceedings of the 6th international conference on Web Reasoning and Rule Systems
Inconsistency management for description logic programs and beyond
RR'13 Proceedings of the 7th international conference on Web Reasoning and Rule Systems
Data repair of inconsistent DL-programs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Description Logic Programs (DL-programs) have been introduced to combine ontological and rule-based reasoning in the context of the Semantic Web. A DL-program loosely combines a Description Logic (DL) ontology with a non-monotonic logic program (LP) such that dedicated atoms in the LP, called DL-atoms, allow for a bidirectional flow of knowledge between the two components. Unfortunately, the information sent from the LP-part to the DL-part might cause an inconsistency in the latter, leading to the trivial satisfaction of every query. As a consequence, in such a case, the answer sets that define the semantics of the DL-program may contain spoiled information influencing the overall deduction. For avoiding unintuitive answer sets, we introduce a refined semantics for DL-programs that is sensitive for inconsistency caused by the combination of DL and LP, and dynamically deactivates rules whenever such an inconsistency would arise. We analyze the complexity of the new semantics, discuss implementational issues and introduce a notion of stratification that guarantees uniqueness of answer sets.