2006 | OriginalPaper | Buchkapitel
Handling Changes of Database Schemas and Corresponding Ontologies
verfasst von : Andreas Kupfer, Silke Eckstein, Karl Neumann, Brigitte Mathiak
Erschienen in: Advances in Conceptual Modeling - Theory and Practice
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Currently, knowledge from biological research is stored in hundreds of databases, counting only public accessible ones. Finding specific data in these is a challenging task which can be supported by ontologies describing them. The maintenance of a corresponding ontology is time consuming manual work, because research database schemas change rapidly. Our project will reduce the work by automating tasks, like a generation process and applying schema changes to the corresponding ontology. We call the proposed method coevolution, because database schema and ontology are allowed to evolve independently without ever losing their connection to each other. Our method consists of initial ontology generation, manual annotation and change propagation steps.