A softbot-based interface to the Internet
Communications of the ACM
Mediation in information systems
ACM Computing Surveys (CSUR)
Infomaster: an information integration system
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Navigational plans for data integration
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Description Logics for Information Integration
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Data Integration under Integrity Constraints
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Xyleme: A Dynamic Warehouse for XML Data of the Web
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
COOPIS '96 Proceedings of the First IFCIS International Conference on Cooperative Information Systems
Knowledge integration through semantic query rewriting
ACS'09 Proceedings of the 9th WSEAS international conference on Applied computer science
| Hi-index | 0.00 |
Data integration by mediation is more practical then centralized data warehouse when data is not static. The use of ontologies in the mediation allows semantic and structural integration. In this paper, we propose a new mediation system based on a hybrid architecture of ontologies modeled according to GLAV (Generalized Local As View) model. Thus, we propose an ascending method for building local and global ontologies, which facilitates the semantic reconciliation between data sources. Moreover, we use OWL (Ontology Web Language) for defining mappings between data sources, and ontologies. We also propose a language to formulate user queries. The query language handles global ontology concepts and local ontologies properties because we assume that the user is expert in its domain. Finally, we propose a query rewriting strategy: queries are decomposed to obtain a set of equivalent subqueries that are sent to the corresponding data sources for execution, and after that recomposed to obtain the final result.