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
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 t
hese models, it would be possible to gain insights about particular
ontology features likely to be reasoner robustness degrading factors.
(More)