9329

Optimizing Similarity Computations for Ontology Matching – Experiences from GOMMA

Michael Hartung, Lars Kolb, Anika Gross, Erhard Rahm
Department of Computer Science, University of Leipzig
9th Intl. Conference on Data Integration in the Life Sciences (DILS), 2013

@article{hartung2013optimizing,

   title={Optimizing Similarity Computations for Ontology Matching-Experiences from GOMMA},

   author={Hartung, Michael and Kolb, Lars and Gro{ss}, Anika and Rahm, Erhard},

   year={2013}

}

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An efficient computation of ontology mappings requires optimized algorithms and significant computing resources especially for large life science ontologies. We describe how we optimized n-gram matching for computing the similarity of concept names and synonyms in our match system GOMMA. Furthermore, we outline how to enable a highly parallel string matching on Graphical Processing Units (GPU). The evaluation on the OAEI LargeBio match task demonstrates the high effectiveness of the proposed optimizations and that the use of GPUs in addition to standard processors enables significant performance improvements.
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