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Authors: Nourhène Alaya 1 ; Sadok Ben Yahia 2 and Myriam Lamolle 3

Affiliations: 1 LIASD, IUT of Montreuil, University of Paris 8, LIPAH, Faculty of Sciences of Tunis and University of Tunis, France ; 2 LIPAH, Faculty of Sciences of Tunis and University of Tunis, Tunisia ; 3 LIASD, IUT of Montreuil and University of Paris 8, France

ISBN: 978-989-758-158-8

Keyword(s): Ontology, OWL, Reasoner, Robustness, Supervised Machine Learning, Prediction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Collaboration and e-Services ; Communication and Software Technologies and Architectures ; Data Engineering ; e-Business ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Knowledge Acquisition ; Knowledge Engineering ; Knowledge Engineering and Ontology Development ; Knowledge Representation ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Ontology Engineering ; Semantic Web ; Soft Computing ; Symbolic Systems

Abstract: Reasoning with ontologies is one of the core tasks of research in Description Logics. A variety of reasoners with highly optimized algorithms have been developed to allow inference tasks on expressive ontology languages such as OWL (DL). However, unexpected behaviours of reasoner engines is often observed in practice. Both reasoner time efficiency and result correctness would vary across input ontologies, which is hardly predictable even for experienced reasoner designers. Seeking for better understanding of reasoner empirical behaviours, we propose to use supervised machine learning techniques to automatically predict reasoner robustness from its previous running. For this purpose, we introduced a set of comprehensive ontology features. We conducted huge body of experiments for 6 well known reasoners and using over 1000 ontologies from the ORE’2014 corpus. Our learning results show that we could build highly accuracy reasoner robustness predictive models. Moreover, by interpreting th ese models, it would be possible to gain insights about particular ontology features likely to be reasoner robustness degrading factors. (More)

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Paper citation in several formats:
Alaya N., Ben Yahia S. and Lamolle M. (2015). Predicting the Empirical Robustness of the Ontology Reasoners based on Machine Learning Techniques.In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KEOD, (IC3K 2015) ISBN 978-989-758-158-8, pages 61-73. DOI: 10.5220/0005599800610073

@conference{keod15,
author={Nourhène Alaya and Sadok Ben Yahia and Myriam Lamolle},
title={Predicting the Empirical Robustness of the Ontology Reasoners based on Machine Learning Techniques},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KEOD, (IC3K 2015)},
year={2015},
pages={61-73},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005599800610073},
isbn={978-989-758-158-8},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KEOD, (IC3K 2015)
TI - Predicting the Empirical Robustness of the Ontology Reasoners based on Machine Learning Techniques
SN - 978-989-758-158-8
AU - Alaya N.
AU - Ben Yahia S.
AU - Lamolle M.
PY - 2015
SP - 61
EP - 73
DO - 10.5220/0005599800610073

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