A novel three-level architecture for large data warehouses
Journal of Systems Architecture: the EUROMICRO Journal
Deriving ``Sub-source'' Similarities from Heterogeneous, Semi-structured Information Sources
CooplS '01 Proceedings of the 9th International Conference on Cooperative Information Systems
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
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
The PROMPT suite: interactive tools for ontology merging and mapping
International Journal of Human-Computer Studies
Methods for automated concept mapping between medical databases
Journal of Biomedical Informatics
Semantic-integration research in the database community
AI Magazine - Special issue on semantic integration
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
eTuner: tuning schema matching software using synthetic scenarios
The VLDB Journal — The International Journal on Very Large Data Bases
Relating taxonomies with regulations
dg.o '08 Proceedings of the 2008 international conference on Digital government research
Regulation retrieval using industry specific taxonomies
Artificial Intelligence and Law
A model for matching and integrating heterogeneous relational biomedical databases schemas
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Integrating schemas of heterogeneous relational databases through schema matching
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Extraction and exploitation of intensional knowledge from heterogeneous information sources: semi-automatic approaches and tools
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The availability of automatic tools for inferring semantics from database schemes is very relevant in designing large Cooperative Information System applications involving many information sources. Deriving semantics from existing data sources exploits properties of objects belonging to different input schemes (interscheme properties), such as synonymies, homonymies, type conflicts, and subscheme similarities. This paper gives a contribution in this context by proposing a collection of graph-based techniques for a uniform derivation of all interscheme properties. All techniques are characterized by a common core consisting in the computation of a maximum weight matching on suitable bipartite graphs. The computation of the maximum weight matching is based on a suitable metrics which is used to measure object semantic similarities. A running example is provided to illustrate the approach.