An approach to parallel class expression learning

Abstract

We propose a Parallel Class Expression Learning algorithm that is inspired by the OWL Class Expression Learner (OCEL) and its extension - Class Expression Learning for Ontology Engineering (CELOE) - proposed by Lehmann et al. in the DL-Learner framework. Our algorithm separates the computation of partial definitions from the aggregation of those solutions to an overall complete definition, which lends itself to parallelisation. Our algorithm is implemented based on the DL-Learner infrastructure and evaluated using a selection of datasets that have been used in other ILP systems. It is shown that the proposed algorithm is suitable for learning problems that can only be solved by complex (long) definitions. Our approach is part of an ontology-based abnormality detection framework that is developed to be used in smart homes. © 2012 Springer-Verlag.