Maintaining knowledge about temporal intervals
Communications of the ACM
View Integration: A Step Forward in Solving Structural Conflicts
IEEE Transactions on Knowledge and Data Engineering
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Answering queries using views: A survey
The VLDB Journal — The International Journal on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Representing and reasoning about mappings between domain models
Eighteenth national conference on Artificial intelligence
Discovering Direct and Indirect Matches for Schema Elements
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Rondo: a programming platform for generic model management
Proceedings of the 2003 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
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Composing schema mappings: second-order dependencies to the rescue
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Merging models based on given correspondences
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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Defining and composing mappings are fundamental operations required in any data sharing architecture (e.g data warehouse, data integration) Mapping composition is used to generate new mappings from existing ones and is useful when no direct mapping is available The complexity of mapping composition depends on the amount of syntactic and semantic information in the mapping The composition of mappings has proven to be inefficient to compute in many situations unless the mappings are simplified to binary relationships that represent “similarity” between concepts Our contribution is an algorithm for composing metadata mappings that capture explicit semantics in terms of binary relationships Our approach allows the hard cases of mapping composition to be detected and semi-automatically resolved, and thus reduces the manual effort required during composition We demonstrate how the mapping composition algorithm is used to produce a direct mapping between schemas from independently produced schema-to-ontology mappings An experimental evaluation shows that composing semantic mappings results in a more accurate composition result compared to composing mappings as morphisms.