Extensional Knowledge for semantic query optimization in a mediator based system

Query processing in global information systems integrating multiple heterogeneous sources is a challenging issue in relation to the effective extraction of information available on-line. In this paper we propose intelligent, tool-supported techniques for querying global information systems integrating both structured and semistructured data sources. The techniques have been developed in the environment of a data integration, wrapper/mediator based system, MOMIS, and try to achieve the goal of optimized query reformulation w.r.t local sources. The developed techniques rely on the availability of integration knowledge whose semantics is expressed in terms of description logics. Integration knowledge includes local source schemata, a virtual mediated schema and its mapping descriptions, that is semantic mappings w.r.t. the underlying sources both at the intensional and extensional level. Mapping descriptions, obtained as a result of the semi-automatic integration process of multiple heterogeneous sources developed for the MOMIS system, include, unlike previous data integration proposals, extensional intra/interschema knowledge. Extensional knowledge is exploited to perform semantic query optimization in a mediator based system as it allows to devise an optimized query reformulation method. The techniques are under development in the MOMIS system but can be applied, in general, to data integration systems including extensional intra/interschema knowledge in mapping descriptions