Laadhar, Amir and Ghozzi, Faiza and Ichise, Ryutaro and Megdiche Bousarsar, Imen
and Ravat, Franck
and Teste, Olivier
Partitioning and Matching Tuning of Large Biomedical Ontologies.
(2018)
In: 13th International Workshop on Ontology Matching co-located with the 17th International Semantic Web Conference (OM 2018), 8 October 2018 (Monterey, CA, United States).
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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 415kB |
Official URL: http://ceur-ws.org/Vol-2288/om2018_poster3.pdf
Abstract
Large biomedical ontologies such as SNOMED CT, NCI, and FMA are exten-sively employed in the biomedical domain. These complex ontologies are basedon diverse modelling views and vocabularies. We define an approach that breaksup a large ontology alignment problem into a set of smaller matching tasks.We coupled this approach with an automated tuning process, which generatesthe adequate thresholds of the available similarity measure for any biomedicalmatching task. Experiments demonstrate that the coupling between ontologypartitioning and threshold tuning outperforms the existing approaches.
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