Software Agent Oriented Information Integration System in Semantic Web

Developing an information-sharing capability across distributed heterogeneous data sources remains a significant challenge. Ontology based approach 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 ontology offers the capability to work with distributed data as if it were located in a central repository. This information 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 information sharing. One way to solve this problem is to construct a series of ontology, 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 oriented information integration system that demonstrates a flexible and dynamic approach for the fusion of data across combinations of participating heterogeneous data sources to maximize information sharing. The software agent generates the largest intersection of shared data across any selected subset of data sources. This ontology-based agent approach maximizes information sharing by dynamically generating common ontology over the data sources of interest. Our county, Myanmar, have seven states. Each state has one agricultural research center. And so, our approach was validated using data provided by seven (disparate) agricultural research centers by defining a local ontology for each research center (i.e., data source). The Ministry of Agriculture & Irrigation (MOAI) manages the all states´ agricultural research centers, each of which has evolved a variety of agricultural models for managing research proposals over the past decade.- - Because of the historical nature of these evolutions, both the agricultural models and their associated (heterogeneous) data collections are deeply rooted. A system was needed that could merge data from the heterogeneous systems as if the data was gathered and stored in a centralized repository. In our approach, the ontology is used to specify how to format the data using XML to make it suitable for query. Software agents provide the ability from the data sources to dynamically form local ontology for each research center. By using ontology based software agent, the cost of developing this ontology is reduced while providing the broadest possible access to available data sources