ACM Computing Surveys (CSUR)
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Information Retrieval
Global Viewing of Heterogeneous Data Sources
IEEE Transactions on Knowledge and Data Engineering
Information Integration: The MOMIS Project Demonstration
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Statistical schema matching across web query interfaces
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
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
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
Discovering complex matchings across web query interfaces: a correlation mining approach
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Meaningful labeling of integrated query interfaces
VLDB '06 Proceedings of the 32nd 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
Instance-based schema matching for web databases by domain-specific query probing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Corpus-based knowledge representation
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A survey of schema-based matching approaches
Journal on Data Semantics IV
Clustering structured web sources: a schema-based, model-differentiation approach
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
A Prioritized Collective Selection Strategy for Schema Matching across Query Interfaces
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
A query interface matching approach based on extended evidence theory for deep web
Journal of Computer Science and Technology
ETTA-IM: A deep web query interface matching approach based on evidence theory and task assignment
Expert Systems with Applications: An International Journal
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Schema matching is a crucial step in data integration. Many approaches to schema matching have been proposed so far. Different types of information about schemas, including structures, linguistic features and data types, etc have been used to match attributes between schemas. Relying on a single aspect of information about schemas for schema matching is not sufficient. Approaches have been proposed to combine multiple matchers taking into account different aspects of information about schemas. Weights are usually assigned to individual matchers so that their match results can be combined taking into account their different levels of importance. However, these weights have to be manually generated and are domain-dependent. We propose a new approach to combining multiple matchers using the Dempster-Shafer theory of evidence, which finds the top-k attribute correspondences of each source attribute from the target schema. We then make use of some heuristics to resolve any conflicts between the attribute correspondences of different source attributes. Our experimental results show that our approach is highly effective.