Efficient algorithms for finding maximum matching in graphs
ACM Computing Surveys (CSUR)
A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
Conceptual database design: an Entity-relationship approach
Conceptual database design: an Entity-relationship approach
Semantic vs. structural resemblance of classes
ACM SIGMOD Record
Automated resolution of semantic heterogeneity in multidatabases
ACM Transactions on Database Systems (TODS)
Using semantic values to facilitate interoperability among heterogeneous information systems
ACM Transactions on Database Systems (TODS)
An automatic technique for detecting type conflicts in database schemes
Proceedings of the seventh international conference on Information and knowledge management
System-Guided View Integration for Object-Oriented Databases
IEEE Transactions on Knowledge and Data Engineering
View Integration: A Step Forward in Solving Structural Conflicts
IEEE Transactions on Knowledge and Data Engineering
Semantic Dictionary Design for Database Interoperability
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Information Integration Using Logical Views
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Automatic Derivation of Terminological Properties from Database Schemes
DEXA '98 Proceedings of the 9th International Conference on Database and Expert Systems Applications
Semi-automatic Extraction of Hyponymies and Overlappings from Heterogeneous Database Schemes
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
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This paper proposes an automatic, probabilistic approach to the detection of type conflicts and object cluster similarities in database schemes. The type of an object indicates if it is an entity, a relationship or an attribute; type conflicts indicate the existence of objects representing the same concept yet having different types. Object cluster similarities denote similitudes between portions of different schemes. The method we are proposing here is based on considering pairs of objects having different types (resp. pairs of clusters), belonging to different schemes and on measuring their similarity. To this purpose object (resp. cluster) structures as well as object (resp. cluster) neighborhoods are analyzed to verify similitudes and differences.