Hybrid reasoning for ontology classification

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DOIResolve DOI: http://doi.org/10.1007/978-3-642-21043-3_44
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TypeBook Chapter
Proceedings titleAdvances in Artificial Intelligence : 24th Canadian Conference on Artificial Intelligence, Canadian AI 2011, St. John’s, Canada, May 25-27, 2011. Proceedings
Series titleLecture Notes In Computer Science; Volume 6657
Conference24th Canadian Conference on Artificial Intelligence (Canadian AI 2011), May 25-27, 2011, St. John’s, Canada.
Pages372376; # of pages: 5
Subjecthybrid reasoning; complex ontology; classification; tableau; resolution
AbstractOntology classification is an essential reasoning task for ontology based systems. Tableau and resolution are two dominant types of reasoning procedures for ontology reasoning. Complex ontologies are often built on more expressive description logics and are usually highly cyclic. When reasoning complex ontologies, the both approaches may have difficulties in terms of reasoning results and performance, but for different ontology types. In this research, we investigate a hybrid reasoning approach, which will employ well-defined strategies to decompose and modify a complex ontology into subsets of ontologies based on capabilities of different reasoners, process the subsets with suitable individual reasoners, and combine such individual classification results into the overall classification result. The objective of our approach is to detect more subsumption relationships than individual reasoners for complex ontologies, and improve overall reasoning performance.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNational Research Council Canada; NRC Institute for Information Technology
Peer reviewedYes
NPARC number21254669
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Record identifierc4b4c7c5-c594-4f96-8226-5213ba755ad3
Record created2013-02-26
Record modified2016-06-22
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