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Transforming database schemas into an ontology language opens the door wide to the many advantages offered by the Semantic Web. Industries in particular can benefit from intelligent systems (e.g., decision-support systems) arising from such transformations. In this paper, we propose a semi-automated algorithm to transform data to the ontology language, OWL, while taking advantage of the actual data stored in a database schema. These data are used to discover hidden patterns in a database schema with a minimal level of human involvement. Such an approach also ensures improved mappings for relatively loosely structured database schema. The evaluation results on a simple DBLP database schema show improved effectiveness of such transformations.