Managing semantic heterogeneity in databases: a theoretical prospective
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Data & Knowledge Engineering
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Multivariate Statistical Methods: A First Course
Multivariate Statistical Methods: A First Course
Schema Mapping as Query Discovery
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
Autoplex: Automated Discovery of Content for Virtual Databases
CooplS '01 Proceedings of the 9th International Conference on Cooperative Information Systems
A Model Theory for Generic Schema Management
DBPL '01 Revised Papers from the 8th International Workshop on Database Programming Languages
Representing and reasoning about mappings between domain models
Eighteenth national conference on Artificial intelligence
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Generic Model Management: Concepts And Algorithms (Lecture Notes in Computer Science)
Generic Model Management: Concepts And Algorithms (Lecture Notes in Computer Science)
A framework for modeling and evaluating automatic semantic reconciliation
The VLDB Journal — The International Journal on Very Large Data Bases
Making holistic schema matching robust: an ensemble approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Automatic ontology matching using application semantics
AI Magazine - Special issue on semantic integration
eTuner: tuning schema matching software using synthetic scenarios
The VLDB Journal — The International Journal on Very Large Data Bases
Information retrieval and machine learning for probabilistic schema matching
Information Processing and Management: an International Journal
A composite approach to automating direct and indirect schema mappings
Information Systems
Rank Aggregation for Automatic Schema Matching
IEEE Transactions on Knowledge and Data Engineering
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Bootstrapping ontology alignment methods with APFEL
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Holistic schema matching for web query interfaces
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Soundness of schema matching methods
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
A survey of schema-based matching approaches
Journal on Data Semantics IV
Managing uncertainty in schema matching with top-k schema mappings
Journal on Data Semantics VI
Preference-Based Uncertain Data Integration
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
A Flexible Approach for Planning Schema Matching Algorithms
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
A Survey on Uncertainty Management in Data Integration
Journal of Data and Information Quality (JDIQ)
Tuning the ensemble selection process of schema matchers
Information Systems
Markov network based ontology matching
Journal of Computer and System Sciences
Actively soliciting feedback for query answers in keyword search-based data integration
Proceedings of the VLDB Endowment
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Schema matching is the task of matching between concepts describing the meaning of data in various heterogeneous, distributed data sources. With many heuristics to choose from, several tools have enabled the use of schema matcher ensembles, combining principles by which different schema matchers judge the similarity between concepts. In this work, we investigate means of estimating the uncertainty involved in schema matching and harnessing it to improve an ensemble outcome. We propose a model for schema matching, based on simple probabilistic principles. We then propose the use of machine learning in determining the best mapping and discuss its pros and cons. Finally, we provide a thorough empirical analysis, using both real-world and synthetic data, to test the proposed technique. We conclude that the proposed heuristic performs well, given an accurate modeling of uncertainty in matcher decision making.