Predicting reasoning performance using ontology metrics

  • Authors:

  • Yong-Bin Kang;Yuan-Fang Li;Shonali Krishnaswamy

  • Affiliations:

  • Faculty of IT, Monash University, Australia;Faculty of IT, Monash University, Australia;Faculty of IT, Monash University, Australia,Institute for Infocomm Research, A*STAR, Singapore

  • Venue:
  • ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
  • Year:
  • 2012

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Abstract

A key issue in semantic reasoning is the computational complexity of inference tasks on expressive ontology languages such as OWL DL and OWL 2 DL. Theoretical works have established worst-case complexity results for reasoning tasks for these languages. However, hardness of reasoning about individual ontologies has not been adequately characterised. In this paper, we conduct a systematic study to tackle this problem using machine learning techniques, covering over 350 real-world ontologies and four state-of-the-art, widely-used OWL 2 reasoners. Our main contributions are two-fold. Firstly, we learn various classifiers that accurately predict classification time for an ontology based on its metric values. Secondly, we identify a number of metrics that can be used to effectively predict reasoning performance. Our prediction models have been shown to be highly effective, achieving an accuracy of over 80%.