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

Yong-Bin Kang, Jeff Z. Pan, Shonali Krishnaswamy, Wudhichart Sawangphol, Yuan-Fang Li

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

20 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of the 28th Conference on Artificial Intelligence (AAAI 2014)
PublisherAAAI Press
Number of pages7
Publication statusPublished - Aug 2014
Event28th AAAI Conference on Artificial Intelligence - Quebec Convention Center, Quebec, Canada
Duration: 27 Jul 201431 Jul 2014


Conference28th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-14

Bibliographical note

We would like to thank Rafael S. Gonc¸alves for providing us with the ontology and hotspots dataset. We are also grateful to Yuan Ren for the many helpful discussions.


  • Ontology
  • Reasoning
  • Prediction
  • Performance hotspots


Dive into the research topics of 'How Long Will It Take? Accurate Prediction of Ontology Reasoning Performance'. Together they form a unique fingerprint.

Cite this