Developing a knowledge-sharing capability across distributed heterogeneous data sources remains a significant challenge. Ontology-based approaches to this problem show promise by resolving heterogeneity, if the participating data owners agree to use a common ontology (i.e., a set of common attributes). Such common ontologies offer the capability to work with distributed data as if it were located in a central repository. This knowledge sharing may be achieved by determining the intersection of similar concepts from across various heterogeneous systems. However, if information is sought from a subset of the participating data sources, there may be concepts common to the subset that are not included in the full common ontology, and therefore are unavailable for knowledge sharing. One way to solve this problem is to construct a series of ontologies, one for each possible combination of data sources. In this way, no concepts are lost, but the number of possible subsets is prohibitively large. This paper describes a software agent case study that demonstrates a flexible and dynamic approach for the fusion of data across combinations of participating heterogeneous data sources to maximize knowledge sharing. The software agents generate the largest intersection of shared data across any selected subset of data sources. This ontology-based agent approach maximizes knowledge sharing by dynamically generating common ontologies over the data sources of interest. The approach was validated using data provided by five (disparate) national laboratories by defining a local ontology for each laboratory (i.e., data source). In this experiment, the ontologies are used to 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
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