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Introducing defeasibility into OWL ontologies

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dc.contributor.author Casini, G
dc.contributor.author Meyer, T
dc.contributor.author Moodley, K
dc.contributor.author Sattler, U
dc.contributor.author Varzinczak, I
dc.date.accessioned 2016-02-23T09:13:26Z
dc.date.available 2016-02-23T09:13:26Z
dc.date.issued 2015-10
dc.identifier.citation Casini, G, Meyer, T, Moodley, K, Sattler, U and Varzinczak, I. 2015. Introducing defeasibility into OWL ontologies. In: The 14th International Semantic Web Conference, Betlehem, Pennsylvania, 11-15 October 2015 en_US
dc.identifier.uri http://iswc2015.semanticweb.org/program/accepted-papers
dc.identifier.uri http://link.springer.com/chapter/10.1007%2F978-3-319-25010-6_27#page-2
dc.identifier.uri http://hdl.handle.net/10204/8417
dc.description The 14th International Semantic Web Conference, Betlehem, Pennsylvania, 11-15 October 2015. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website en_US
dc.description.abstract In recent years, various approaches have been developed for representing and reasoning with exceptions in OWL. The price one pays for such capabilities, in terms of practical performance, is an important factor that is yet to be quantified comprehensively. A major barrier is the lack of naturally occurring ontologies with defeasible features - the ideal candidates for evaluation. Such data is unavailable due to absence of tool support for representing defeasible features. In the past, defeasible reasoning implementations have favoured automated generation of defeasible ontologies. While this suffices as a preliminary approach, we posit that a method somewhere in between these two would yield more meaningful results. In this work, we describe a systematic approach to modify real-world OWL ontologies to include defeasible features, and we apply this to the Manchester OWL Repository to generate defeasible ontologies for evaluating our reasoner DIP (Defeasible-Inference Platform). The results of this evaluation are provided together with some insights into where the performance bottle-necks lie for this kind of reasoning. We found that reasoning was feasible on the whole, with surprisingly few bottle-necks in our evaluation. en_US
dc.language.iso en en_US
dc.publisher Springer International Publishing en_US
dc.relation.ispartofseries Workflow;15629
dc.subject OWL ontologies en_US
dc.subject Defeasible ontologies en_US
dc.subject Artificial Intelligence en_US
dc.subject AI en_US
dc.title Introducing defeasibility into OWL ontologies en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Casini, G., Meyer, T., Moodley, K., Sattler, U., & Varzinczak, I. (2015). Introducing defeasibility into OWL ontologies. Springer International Publishing. http://hdl.handle.net/10204/8417 en_ZA
dc.identifier.chicagocitation Casini, G, T Meyer, K Moodley, U Sattler, and I Varzinczak. "Introducing defeasibility into OWL ontologies." (2015): http://hdl.handle.net/10204/8417 en_ZA
dc.identifier.vancouvercitation Casini G, Meyer T, Moodley K, Sattler U, Varzinczak I, Introducing defeasibility into OWL ontologies; Springer International Publishing; 2015. http://hdl.handle.net/10204/8417 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Casini, G AU - Meyer, T AU - Moodley, K AU - Sattler, U AU - Varzinczak, I AB - In recent years, various approaches have been developed for representing and reasoning with exceptions in OWL. The price one pays for such capabilities, in terms of practical performance, is an important factor that is yet to be quantified comprehensively. A major barrier is the lack of naturally occurring ontologies with defeasible features - the ideal candidates for evaluation. Such data is unavailable due to absence of tool support for representing defeasible features. In the past, defeasible reasoning implementations have favoured automated generation of defeasible ontologies. While this suffices as a preliminary approach, we posit that a method somewhere in between these two would yield more meaningful results. In this work, we describe a systematic approach to modify real-world OWL ontologies to include defeasible features, and we apply this to the Manchester OWL Repository to generate defeasible ontologies for evaluating our reasoner DIP (Defeasible-Inference Platform). The results of this evaluation are provided together with some insights into where the performance bottle-necks lie for this kind of reasoning. We found that reasoning was feasible on the whole, with surprisingly few bottle-necks in our evaluation. DA - 2015-10 DB - ResearchSpace DP - CSIR KW - OWL ontologies KW - Defeasible ontologies KW - Artificial Intelligence KW - AI LK - https://researchspace.csir.co.za PY - 2015 T1 - Introducing defeasibility into OWL ontologies TI - Introducing defeasibility into OWL ontologies UR - http://hdl.handle.net/10204/8417 ER - en_ZA


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