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Ontology Reasoning with Large Data Repositories

  • Stijn Heymans
  • Li Ma
  • Darko Anicic
  • Zhilei Ma
  • Nathalie Steinmetz
  • Yue Pan
  • Jing Mei
  • Achille Fokoue
  • Aditya Kalyanpur
  • Aaron Kershenbaum
  • Edith Schonberg
  • Kavitha Srinivas
  • Cristina Feier
  • Graham Hench
  • Branimir Wetzstein
  • Uwe Keller
Part of the Computing for Human Experience book series (ADSW, volume 7)

Reasoning with large amounts of data together with ontological knowledge is becoming a pertinent issue. In this chapter, we will give an overviewof wellknown ontology repositories, including native stores and database based stores, and highlight strengths and limitations of each store. We take Minerva as an example to analyze ontology storage in databases in depth, as well as to discuss efficient indexes for scaling up ontology repositories. We then discuss a scalable reasoning method for handling expressive ontologies, as well as summarize other similar approaches. We will subsequently delve into the details of one particular ontology language based on Description Logics called WSML-DL and show that reasoning with this language can be done by a transformation from WSML-DL to OWL DL and support all main DL-specific reasoning tasks. Finally, we illustrate reasoning and its relevance by showing a reasoning example in a practical business context by presenting the Semantic Business Process Repository (SBPR) for systemical management of semantic business process models. As part of this, we analyze the main requirements on a such a repository. We then compare different approaches for storage mechanisms for this purpose and show how a RDBMS in combination with the IRIS inference engine provides a suitable solution that deals well with the expressiveness of the query language and the required reasoning capabilities even for large amounts of instance data.

Keywords

business repository IRIS OWL DL reasoning with large datasets Semantic Business Process Management WSML DL 

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Stijn Heymans
    • 1
  • Li Ma
    • 2
  • Darko Anicic
    • 1
  • Zhilei Ma
    • 3
  • Nathalie Steinmetz
    • 1
  • Yue Pan
    • 2
  • Jing Mei
    • 2
  • Achille Fokoue
    • 4
  • Aditya Kalyanpur
    • 4
  • Aaron Kershenbaum
    • 4
  • Edith Schonberg
    • 4
  • Kavitha Srinivas
    • 4
  • Cristina Feier
    • 1
  • Graham Hench
    • 1
  • Branimir Wetzstein
    • 3
  • Uwe Keller
    • 2
  1. 1.Digital Enterprise Research InstituteUniversity of InnsbruckAustria
  2. 2.IBM China Research LabChina
  3. 3.Institute of Architecture of Application Systems (IAAS)University of StuttgartGermany
  4. 4.IBM Watson Research CenterYorktown HeightsUSA

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