Description

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.

Keywords

• Parallel Ontology Matching
• GPU

BibTex

@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}
}