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This case study demonstrates a flexible and dynamic approach for fusing data across combinations of participating heterogeneous sources to maximize knowledge sharing. Software agents are used to generate the largest intersection of shared data across any selected data source subset. This ontology-based agent approach maximizes knowledge sharing by dynamically generating common ontologies for the data sources of interest. We validated our approach using (disparate) data sets provided by five national laboratories. A local ontology was defined for each laboratory data source. The ontologies specify how to format the data using XML to make it suitable for query. Consequently, software agents are empowered to provide the ability to dynamically form local ontologies from the data sources. In this way, the cost of developing these ontologies is reduced while providing the broadest possible access to available data sources.