Partitioning and Matching Tuning of Large Biomedical Ontologies

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.