Exploiting extensional knowledge for query reformulation and object fusion in a data integration 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 two main goals: optimized query reformulation w.r.t local sources and object fusion, i.e. grouping together information (from the same or different sources) about the same real-world entity. The developed techniques rely on the availability of integrationknowledge, i.e. 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 detect extensionally overlapping classes and to discover implicit join criteria among classes, which enables the goals of optimized query reformulation and object fusion to be achieved.The techniques have been implemented in the MOMIS system but can be applied, in general, to data integration systems including extensional intra/interschema knowledge in mapping descriptions