Mappings between related ontologies are increasingly used to support data integra-tion and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life sciences [HTR11]. We therefore analyze how mappings between popular life science ontologies evolve for different match algorithms. We also evaluate which semantic ontology changes primarily affect the mappings. Details about our evolution models, measures and detailed evaluation results can be found in [GHTR12].
We evaluated ontology and mapping evolution for three real-world life science domains (Anatomy, Molecular Biology and Chemistry) and took four match strategies into account. Our analysis results show that especially Molecular Biology and Chemistry underlie heavy ontology extensions and revisions whereas Anatomy can be considered as relatively stable. The results indicate a significant correlation between ontology and mapping changes depending on the utilized match strategy and mapping coverage. For the metadata-based match strategies under investigation, a structural Context matcher produced rather unstable results whereas mappings based on Name matching were relatively stable. As expected, ontology extensions primarily lead to correspondence additions and information reducing ontology changes primarily lead to the removal of correspondences. Ontology revisions play an important role and result in both the addition and deletion of correspondences. The results can be valuable for users working with ontology mappings, e.g., one can learn from past ontology/mapping changes and their correlation to estimate possible mapping changes if new ontology versions become available.
In future work, we plan to investigate how known ontology changes can be used to semi-automatically adapt ontology mappings without a completely new mapping determination.