Hybrid reasoning for ontology classification

  1. Get@NRC: Hybrid reasoning for ontology classification (Opens in a new window)
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), Collocated with the 37th Graphics Interface Conference (GI 2011) and the 8th Canadian Conference on Computer and Robot Vision (CRV 2011), May 25-27, 2011, St. John's, NL, Canada
Pages372376; # of pages: 5
SubjectClassification; Complex ontology; Hybrid reasonings; Resolution; Tableau; Artificial intelligence; Computer vision; Data description; Intelligent robots; Interfaces (computer); Ontology
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. © 2011 Springer-Verlag.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNRC Institute for Information Technology; Information and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number21271626
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Record identifier386271a4-e035-471c-9956-089e206521c9
Record created2014-03-24
Record modified2016-06-22
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