Attributive concept descriptions with complements
Artificial Intelligence
A relational model of data for large shared data banks
Communications of the ACM - Special 25th Anniversary Issue
Database Management Systems
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
The Description Logic Handbook
The Description Logic Handbook
Unifying Reasoning and Search to Web Scale
IEEE Internet Computing
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
What Is Approximate Reasoning?
RR '08 Proceedings of the 2nd International Conference on Web Reasoning and Rule Systems
Approximate OWL-Reasoning with Screech
RR '08 Proceedings of the 2nd International Conference on Web Reasoning and Rule Systems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Resolution-Based approximate reasoning for OWL DL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
OWLIM – a pragmatic semantic repository for OWL
WISE'05 Proceedings of the 2005 international conference on Web Information Systems Engineering
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With the development of more expressive description logics (DLs) for the Web Ontology Language OWL the question arises how we can properly deal with the high computational complexity for efficient reasoning. In application cases that require scalable reasoning with expressive ontologies, non-standard reasoning solutions such as approximate reasoning are necessary to tackle the intractability of reasoning in expressive DLs. In this paper, we are concerned with the approximation of the reasoning task of instance retrieval on DL knowledge bases, trading correctness of retrieval results for gain of speed. We introduce our notion of an approximate concept extension and we provide implementations to compute an approximate answer for a concept query by a suitable mapping to efficient database operations. Furthermore, we report on experiments of our approach on instance retrieval with the Wine ontology and discuss first results in terms of error rate and speed-up.