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Improving OWL RL Reasoning in N3 by Using Specialized Rules

  • Dörthe Arndt
  • Ben De Meester
  • Pieter Bonte
  • Jeroen Schaballie
  • Jabran Bhatti
  • Wim Dereuddre
  • Ruben Verborgh
  • Femke Ongenae
  • Filip De Turck
  • Rik Van de Walle
  • Erik Mannens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9557)

Abstract

Semantic Web reasoning can be a complex task: depending on the amount of data and the ontologies involved, traditional OWL DL reasoners can be too slow to face problems in real time. An alternative is to use a rule-based reasoner together with the OWL RL/RDF rules as stated in the specification of the OWL 2 language profiles. In most cases this approach actually improves reasoning times, but due to the complexity of the rules, not as much as it could. In this paper we present an improved strategy: based on the TBoxes of the ontologies involved in a reasoning task, we create more specific rules which then can be used for further reasoning. We make use of the EYE reasoner and its logic Notation3. In this logic, rules can be employed to derive new rules which makes the rule creation a reasoning step on its own. We evaluate our implementation on a semantic nurse call system. Our results show that adding a pre-reasoning step to produce specialized rules improves reasoning times by around 75 %.

Keywords

Notation3 Rule-based reasoning OWL 2 RL 

Notes

Acknowledgements

The research activities described in this paper were funded by Ghent University, iMinds, the IWT Flanders, the FWO-Flanders, and the European Union, in the context of the project “ORCA”, which is a collaboration of Televic Healthcare, Internet-Based Communication Networks and Services (IBCN), and Data Science Lab (DSLab).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dörthe Arndt
    • 1
  • Ben De Meester
    • 1
  • Pieter Bonte
    • 2
  • Jeroen Schaballie
    • 2
  • Jabran Bhatti
    • 3
  • Wim Dereuddre
    • 3
  • Ruben Verborgh
    • 1
  • Femke Ongenae
    • 2
  • Filip De Turck
    • 2
  • Rik Van de Walle
    • 1
  • Erik Mannens
    • 1
  1. 1.Ghent University – iMinds – Data Science LabGhentBelgium
  2. 2.IBCN Research Group, INTEC, Ghent University – iMindsGhentBelgium
  3. 3.Televic HealthcareIzegemBelgium

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