Query Translation for Ontology-extended Data Sources

The problem of translating a query specified in a user data content ontology into queries that can be answered by the individual data sources is an important challenge in data integration in e-science applications. We develop the notions of semantics-preserving query translation and maximally informative query translation in such a setting. We describe an algorithm for maximally informative query translation and its implementation in INDUS, a suite of open source software tools for integrated access to semantically heterogeneous data sources. We summarize experimental results that demonstrate the scalability of the proposed approach with very large ontologies and mappings between ontologies.

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