How Long Will It Take? Accurate Prediction of Ontology Reasoning Performance


  • Yong-Bin Kang Monash University
  • Jeff Z. Pan University of Aberdeen
  • Shonali Krishnaswamy Institute for Infocomm Research
  • Wudhichart Sawangphol Monash University
  • Yuan-Fang Li Monash University



ontology, reasoning, prediction, performance hotspots


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 large-scale 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.




How to Cite

Kang, Y.-B., Pan, J. Z., Krishnaswamy, S., Sawangphol, W., & Li, Y.-F. (2014). How Long Will It Take? Accurate Prediction of Ontology Reasoning Performance. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).