This talk is going to discuss the issues of data and knowledge integration. The first part of the talk is going to concentrate on data integration, i.e. combining the data of different data sources by creating a unified representation of these data. Two core problems in data integration are schema matching, i.e. the identification of correspondences, or mappings, between input schema objects, and schema merging, i.e. the creation of a unified schema based on the identified mappings. Our proposed framework to data integration explicitly represents and manages the uncertainty of schema mappings and the uncertainty of the final integrated schema.
The second part of the talk is going to concentrate on the problem of storing and processing large amounts of Semantic Web knowledge. When large quantities of individuals in an ontology need to be processed efficiently, it is natural to consider that the individuals are held in a relational database. Our proposed framework translates an OWL-DL ontology into an active database that can be queried and updated independently of the source ontology. The resulting active database provides type inference for OWL-DL and it is up to 1000 times faster at query answering than other approaches.