An efficient ontology approach for organizing and mapping deep Web resources

To enable effective access to databases on the Web, it is critical to integrate the large scale deep Web sources. Therefore, schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching has some significant limitations until now. And also, there are some problems of the interfaces which often have some hidden regularities over many sources. These regularities can be essentially leveraged in enabling semantics discover of schema matching. In this paper, we focus on the specific problem of semantic heterogeneity between schema matching. We propose a clustering algorithm to organize similar sources with metadata-labeling. Our system includes a same model representation for all relational databases schemas with their own ontologies. In this paper, we represent the experimental results of semantic mapping for two schemas.