Schema Evolution publication categorizer

Matching life science ontologies to determine ontology mappings has recently become an active field of research. The large size of existing ontologies and the application of complex match strategies for obtaining high quality mappings makes ontology matching a resource- and time-intensive process. To improve performance we investigate different approaches for parallel matching on multiple compute nodes. In particular, we consider inter-matcher and intra-matcher parallelism as well as the parallel execution of element- and structure-level matching.

Ontologies are heavily used in life sciences and evolve continuously to incorporate new or changed insights. Often ontology changes affect only specific parts (regions) of ontologies making it valuable for ontology users and applications to know the heavily changed regions on the one hand and stable regions on the other hand. However, the size and complexity of life science ontologies renders manual approaches to localize changing or stable regions impossible. We therefore propose an approach to automatically discover evolving or stable ontology regions.