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Performance Heterogeneity and Approximate Reasoning in Description Logic Ontologies

  • Rafael S. Gonçalves
  • Bijan Parsia
  • Ulrike Sattler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)

Abstract

Due to the high worst case complexity of the core reasoning problem for the expressive profiles of OWL 2, ontology engineers are often surprised and confused by the performance behaviour of reasoners on their ontologies. Even very experienced modellers with a sophisticated grasp of reasoning algorithms do not have a good mental model of reasoner performance behaviour. Seemingly innocuous changes to an OWL ontology can degrade classification time from instantaneous to too long to wait for. Similarly, switching reasoners (e.g., to take advantage of specific features) can result in wildly different classification times. In this paper we investigate performance variability phenomena in OWL ontologies, and present methods to identify subsets of an ontology which are performance-degrading for a given reasoner. When such (ideally small) subsets are removed from an ontology, and the remainder is much easier for the given reasoner to reason over, we designate them “hot spots”. The identification of these hot spots allows users to isolate difficult portions of the ontology in a principled and systematic way. Moreover, we devise and compare various methods for approximate reasoning and knowledge compilation based on hot spots. We verify our techniques with a select set of varyingly difficult ontologies from the NCBO BioPortal, and were able to, firstly, successfully identify performance hot spots against the major freely available DL reasoners, and, secondly, significantly improve classification time using approximate reasoning based on hot spots.

Keywords

Description Logic Approximate Reasoning Knowledge Compilation Performance Heterogeneity Original Ontology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rafael S. Gonçalves
    • 1
  • Bijan Parsia
    • 1
  • Ulrike Sattler
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUnited Kingdom

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