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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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
Pages80-86
Number of pages7
ISBN (Print)978-1-57735-661-5
Publication statusPublished - 31 Aug 2014
Event28th AAAI Conference on Artificial Intelligence - Québec City, Canada
Duration: 27 Jul 201431 Jul 2014

Publication series

Name
PublisherAAAI
ISSN (Electronic)2159-5399

Conference

Conference28th AAAI Conference on Artificial Intelligence
CountryCanada
CityQuébec City
Period27/07/1431/07/14

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