Schema evolution is the increasingly important ability to adapt deployed schemas to
changing requirements. Effective support for schema evolution is challenging since schema …

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the
domain that it models or novel requirements of information systems that it enables. The …

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 …

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 …

With the advances of Artificial Intelligence, the need for annotated data increases. However,
the quality of these annotations can be impacted by the evolution of domain knowledge …

Matching life science ontologies to determine ontology map**s has recently become an
active field of research. The large size of existing ontologies and the application of complex …

Motivation: Ontologies are used in the annotation and analysis of biological data. As
knowledge accumulates, ontologies and annotation undergo constant modifications to …

Ontology is mainly used to capture “the knowledge semantics”. As knowledge is continually
evolving over time, an ontology should evolve accordingly. Its previous states/versions …

Ontology is the chief technology to model domain knowledge and fix its heterogeneity.
Knowledge evolution is unavoidable in all fields, and ontology should reflect such an …

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 …