On matching large life science ontologies in parallel

  • Authors:

  • Anika Gross;Michael Hartung;Toralf Kirsten;Erhard Rahm

  • Affiliations:

  • Department of Computer Science, University of Leipzig and Interdisciplinary Centre for Bioinformatics, University of Leipzig;Department of Computer Science, University of Leipzig and Interdisciplinary Centre for Bioinformatics, University of Leipzig;Interdisciplinary Centre for Bioinformatics, University of Leipzig and Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig;Department of Computer Science, University of Leipzig and Interdisciplinary Centre for Bioinformatics, University of Leipzig

  • Venue:
  • DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
  • Year:
  • 2010

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Abstract

Matching life science ontologies to determine ontology mappings has recently become an active field of research. The large size of existing ontologies and the application of complex match strategies for obtaining high quality mappings makes ontology matching a resource- and time-intensive process. To improve performance we investigate different approaches for parallel matching on multiple compute nodes. In particular, we consider inter-matcher and intramatcher parallelism as well as the parallel execution of element- and structurelevel matching. We implemented a distributed infrastructure for parallel ontology matching and evaluate different approaches for parallel matching of large life science ontologies in the field of anatomy and molecular biology.