A framework for modeling and evaluating automatic semantic reconciliation
The VLDB Journal — The International Journal on Very Large Data Bases
A Probabilistic XML Approach to Data Integration
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Fusion rules for merging uncertain information
Information Fusion
Information retrieval and machine learning for probabilistic schema matching
Information Processing and Management: an International Journal
Data integration with uncertainty
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Managing Uncertainty in Schema Matcher Ensembles
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
Combining Uncertain Outputs from Multiple Ontology Matchers
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
A bayesian network approach to ontology mapping
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
sPLMap: a probabilistic approach to schema matching
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Schema integration based on uncertain semantic mappings
ER'05 Proceedings of the 24th international conference on Conceptual Modeling
Managing uncertainty in schema matching with top-k schema mappings
Journal on Data Semantics VI
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In this paper we present a novel uncertainty-enabledapproach to data integration. Uncertainty is a natural by-product of many automatic data integration processes. In our approach we keep it up to the integrated database, and use it to improve query answering. Our method is based on the concept of preference: we show how preferences can be interpreted and manipulated to produce a global uncertain data source, and discuss the complexity of ranking query results on the integrated database.