Data-driven understanding and refinement of schema mappings
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Information Retrieval
MAFRA - A MApping FRAmework for Distributed Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
On schema matching with opaque column names and data values
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Ontology mapping: the state of the art
The Knowledge Engineering Review
The Knowledge Engineering Review
An interactive clustering-based approach to integrating source query interfaces on the deep Web
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Information preserving XML schema embedding
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Tuning schema matching software using synthetic scenarios
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Semantic-integration research in the database community
AI Magazine - Special issue on semantic integration
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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Most previous schema mapping works focus on creating mappings in specific data models for data transformation, failing to capture a richer set of possible relationships between schema elements. For example, most schema matching approaches might discover that 'TA' in one schema equals 'grad TA' in another one, even though the relationship can be modeled more accurately by saying that 'grad TA' is a specialization of 'TA'. Deepening the mapping semantics in turn allow richer application semantics. This paper presents and proves the effectiveness of SeMap, a system that constructs a complex, semantically richer mapping (including 'Has-a', 'Is-a', 'Associates' and 'Equivalent' relationship types) that can be used across data models. We achieve this goal by: (1) exploiting semantic evidence for possible matches; (2) finding a globally optimal match assignment; (3) identifying the relationship embedded in the selected matches. We implemented our semantic matching approach within a prototype system, SeMap, and showed its accuracy and effectiveness.