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BACKGROUND Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data. RESULTS We present GOMMA, a generic(More)
Life science ontologies evolve frequently to meet new requirements or to better reflect the current domain knowledge. The development and adaptation of large and complex ontologies is typically performed collaboratively by several curators. To effectively manage the evolution of ontologies it is essential to identify the difference (Diff) between ontology(More)
There is an increasing need to interrelate different life science ontol-ogies in order to facilitate data integration or semantic data analysis. Ontology matching aims at a largely automatic generation of mappings between ontolo-gies mostly by calculating the linguistic and structural similarity of their concepts. In this paper we investigate an indirect(More)
Entity matching is an important and difficult step for integrating web data. To reduce the typically high execution time for matching we investigate how we can perform entity matching in parallel on a distributed infrastructure. We propose different strategies to partition the input data and generate multiple match tasks that can be independently executed.(More)
BACKGROUND Numerous ontologies have recently been developed in life sciences to support a consistent annotation of biological objects, such as genes or proteins. These ontologies underlie continuous changes which can impact existing annotations. Therefore, it is valuable for users of ontologies to study the stability of ontologies and to see how many and(More)
Matching life science ontologies to determine ontology mappings has recently become an active field of research. The large size of existing ontolo-gies 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(More)
The continuous evolution of life science ontologies requires the adaptation of their associated mappings. We propose two approaches for tackling this problem in a largely automatic way: (1) a composition-based adaptation relying on the principle of mapping composition and (2) a diff-based adaptation algorithm individually handling change operations to(More)
Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontolo-gies 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. We therefore analyze how mappings(More)