Predicting Reasoner Performance on ABox Intensive OWL 2 EL Ontologies

Jeff Z. Pan, Carlos Bobed, Isa Guclu, Fernando Bobillo, Martin J. Kollingbaum, Eduardo Mena and Yuan-Fang Li
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Jeff Z. Pan: Department of Computing Science, University of Aberdeen, Aberdeen, UK
Carlos Bobed: IRISA/Université de Rennes 1, Rennes, France
Isa Guclu: University of Aberdeen, United Kingdom, Aberdeen, UK
Fernando Bobillo: I3A, University of Zaragoza, Zaragoza, Spain
Martin J. Kollingbaum: University of Aberdeen, Aberdeen, UK
Eduardo Mena: I3A, University of Zaragoza, Zaragoza, Spain
Yuan-Fang Li: Faculty of Information Technology, Monash University, Clayton, VIC, Australia

International Journal on Semantic Web and Information Systems (IJSWIS), 2018, vol. 14, issue 1, 1-30

Abstract: In this article, the authors introduce the notion of ABox intensity in the context of predicting reasoner performance to improve the representativeness of ontology metrics, and they develop new metrics that focus on ABox features of OWL 2 EL ontologies. Their experiments show that taking into account the intensity through the proposed metrics contributes to overall prediction accuracy for ABox intensive ontologies.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jswis0:v:14:y:2018:i:1:p:1-30

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