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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)
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)
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)
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)
Ontologies have become very popular in life sciences and other domains. They mostly undergo continuous changes and new ontology versions are frequently released. However, current analysis studies do not consider the on-tology changes reflected in different versions but typically limit themselves to a specific ontology version which may quickly become(More)
MOTIVATION Ontologies are used in the annotation and analysis of biological data. As knowledge accumulates, ontologies and annotation undergo constant modifications to reflect this new knowledge. These modifications may influence the results of statistical applications such as functional enrichment analyses that describe experimental data in terms of(More)
— Ontologies such as taxonomies, product catalogs or web directories are heavily used and hence evolve frequently to meet new requirements or to better reflect the current instance data of a domain. To effectively manage the evolution of ontologies it is essential to identify the difference (Diff) between two ontology versions. We propose a novel approach(More)
Life science ontologies substantially change over time to meet the requirements of their users and to include the newest domain knowledge. Thus, an important task is to know what has been modified between two versions of an ontology (diff). This diff should contain all performed changes as compact and understandable as possible. We present CODEX (Complex(More)