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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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<a href="file/Hartung_DILS_2013.pptx" style="float:left; margin-left:20px;">
<img title="Presentation@DILS 2013" src="file/Hartung_DILS_2013.png" width="180" height="135" alt="Presentation" style="border:1px solid grey;"/>
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<h2>Keywords</h2>
<ul>
<li>Parallel Ontology Matching</li>
<li>GPU</li>
</ul>

<h2 id="bibtex_heading">BibTex</h2>
<pre id="bibtex_listing">

@inproceedings{DBLP:conf/dils/HartungKGR13,
author = {Michael Hartung and
Lars Kolb and
Anika Gro{\\ss} and
Erhard Rahm},
title = {{Optimizing Similarity Computations for Ontology Matching
- Experiences from GOMMA}},
booktitle = {DILS},
year = {2013},
pages = {81-89},
ee = {http://dx.doi.org/10.1007/978-3-642-39437-9_7},
crossref = {DBLP:conf/dils/2013},
bibsource = {DBLP, http://dblp.uni-trier.de}
}

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