For expressive ontology languages such as OWL 2 DL, classification is a computationally expensive task— 2NEXPTIME-complete in the worst case. Hence, it is highly desirable to be able to accurately estimate classification time, especially for large and complex ontologies. Recently, machine learning techniques have been successfully applied to predicting the reasoning hardness category for a given (ontology, reasoner) pair. In this paper, we further develop predictive models to estimate actual classification time using regression techniques, with ontology metrics as features. Our largescale experiments on 6 state-of-the-art OWL 2 DL reasoners and more than 450 significantly diverse ontologies demonstrate that the prediction models achieve high accuracy, good generalizability and statistical significance. Such prediction models have a wide range of applications. We demonstrate how they can be used to efficiently and accurately identify performance hotspots in a large and complex ontology, an otherwise very time-consuming and resource-intensive task.
|Title of host publication||Proceedings of the 28th Conference on Artificial Intelligence (AAAI 2014)|
|Number of pages||7|
|Publication status||Published - 31 Aug 2014|
|Event||28th AAAI Conference on Artificial Intelligence - Québec City, Canada|
Duration: 27 Jul 2014 → 31 Jul 2014
|Conference||28th AAAI Conference on Artificial Intelligence|
|Period||27/07/14 → 31/07/14|