Yong-Bin Kang, Yuan-Fang Li and Shonali Krishnaswamy, Predicting Reasoning Performance Using Ontology Metrics, in Proceedings of the 11th International Semantic Web Conference (ISWC 2012), Boston, US, November, 2012. [PDF(local)], [PDF(online)]

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%.

BibTex:

    @InProceedings { iswc2012paper-research-36,
      author = { Yong-Bin Kang, Yuan-Fang Li and Shonali Krishnaswamy },
      title = { Predicting Reasoning Performance Using Ontology Metrics },
      booktitle = { Proceedings of the 11th International Semantic Web Conference (ISWC 2012) },
      address = {Boston, US},
      month = { November },
      year = { 2012 },
    }

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