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 proceedingConference PaperResearchpeer-review

    15 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 Twenty-Eighth AAAI Conference on Artificial Intelligence
    EditorsCarla E Brodley, Peter Stone
    Place of PublicationPalo Alto CA USA
    PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
    Number of pages7
    Publication statusPublished - 2014
    EventAAAI Conference on Artificial Intelligence 2014 - Quebec, Canada
    Duration: 27 Jul 201431 Jul 2014
    Conference number: 28th


    ConferenceAAAI Conference on Artificial Intelligence 2014
    Abbreviated titleAAAI 2014
    Internet address

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