The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Complexity and expressive power of logic programming
ACM Computing Surveys (CSUR)
P-SHOQ(D): A Probabilistic Extension of SHOQ(D) for Probabilistic Ontologies in the Semantic Web
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Bayesian semantics for the semantic web
Bayesian semantics for the semantic web
Ontology Matching
Probabilistic description logic programs
International Journal of Approximate Reasoning
Expressive probabilistic description logics
Artificial Intelligence
PR-OWL: A Framework for Probabilistic Ontologies
Proceedings of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006)
DL-Lite: tractable description logics for ontologies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A formal investigation of mapping language for terminological knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Tightly integrated probabilistic description logic programs for the semantic web
ICLP'07 Proceedings of the 23rd international conference on Logic programming
Towards top-k query answering in description logics: the case of DL-Lite
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Effective integration of declarative rules with external evaluations for semantic-web reasoning
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Structure-based causes and explanations in the independent choice logic
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Can OWL and logic programming live together happily ever after?
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Rule-Based Approaches for Representing Probabilistic Ontology Mappings
Uncertainty Reasoning for the Semantic Web I
Description logic programs under probabilistic uncertainty and fuzzy vagueness
International Journal of Approximate Reasoning
Tightly Coupled Probabilistic Description Logic Programs for the Semantic Web
Journal on Data Semantics XII
Uncertainty in the Semantic Web
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
RDFKB: efficient support for RDF inference queries and knowledge management
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Uncertainty Reasoning for the Semantic Web
RR '09 Proceedings of the 3rd International Conference on Web Reasoning and Rule Systems
Hybrid reasoning with rules and ontologies
Semantic techniques for the web
Tightly integrated probabilistic description logic programs for representing ontology mappings
Annals of Mathematics and Artificial Intelligence
Hi-index | 0.00 |
Creating mappings between ontologies is a common way of approaching the semantic heterogeneity problem on the SemanticWeb. To fit into the landscape of semantic web languages, a suitable, logic-based representation formalism for mappings is needed. We argue that such a formalism has to be able to deal with uncertainty and inconsistencies in automatically created mappings. We analyze the requirements for such a formalism, and we propose a novel approach to probabilistic description logic programs as such a formalism, which tightly combines disjunctive logic programs under the answer set semantics with both description logics and Bayesian probabilities. We define the language, and we show that it can be used to resolve inconsistencies and merge mappings from different matchers based on the level of confidence assigned to different rules. Furthermore, we explore the computational aspects of consistency checking and query processing in tightly integrated probabilistic description logic programs. We show that these problems are decidable and computable, respectively, and that they can be reduced to consistency checking and cautious/brave reasoning, respectively, in tightly integrated disjunctive description logic programs. We also analyze the complexity of consistency checking and query processing in the new probabilistic description logic programs in special cases. In particular, we present a special case of these problems with polynomial data complexity.