Gene Ontology (GO) enrichment analysis is ubiquitously used for interpreting high
throughput molecular data and generating hypotheses about underlying biological …

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 …

Software models are iteratively refined, restructured and evolved. The detection and
analysis of changes applied between two versions of a model are one of the most important …

As every software artifact, also software models are subject to continuous evolution. The
operations applied between two successive versions of a model are crucial for …

Knowledge-based systems and their ontologies evolve due to different reasons. Ontology
evolution is the adaptation of an ontology and the propagation of these changes to …

Achieving high match accuracy for a large variety of ontologies, considering a single
matcher is often not sufficient for high match quality. Therefore, combining the corresponding …

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 …

Map**s between related ontologies are increasingly used to support data integration and
analysis tasks. Changes in the ontologies also require the adaptation of ontology map**s …

The ever-increasing quantity of data produced in biomedical applications requires the
development of intelligent tools implementing Knowledge Organization Systems (KOS) like …

Like other components of Semantic Web-based applications, ontologies are evolving over
time to reflect changes in the real world. Several of these applications require kee** a full …