Schema Evolution publication categorizer

The development of new techniques and the emergence of new high- throughput tools have led to a new information revolution. The amount and the diversity of the information that need to be stored and processed have led to the adoption of data integration systems in order to deal with information extraction from disparate sources. The mediation between traditional databases and ontologies has been recognized as a cornerstone issue in bringing in legacy data with formal semantic meaning. However, our knowledge evolves due to the rapid scientific development, so ontologies and schemata need to change in order to capture and accommodate such an evolution. When ontologies change, these changes should somehow be rendered and used by the pre-existing data integration systems, a problem that most of the integration systems seem to ignore. In this paper, we review existing approaches for ontology/schema evolution and examine their applicability in a state-of-the-art, ontology-based data integration setting. Then, we show that changes in schemata differ significantly from changes in ontologies. This strengthens our position that current state of the art systems are not adequate for ontology-based data integration. So, we give the requirements for an ideal data integration system that will enable and exploit ontology evolution.