Using decision trees to learn ontology taxonomies from relational databases

Relational databases are widely used; they are at the backend of the majority of information systems. However, these databases are semantically poor. To solve this issue, it is necessary to build ontologies. In this paper, we propose an automatic approach to learn ontologies from relational databases by using classification techniques, more specifically decision trees. Finally, we evaluate our approach by conducting tests and comparing our results with results from previous works. The results were satisfactory in terms of extracting taxonomies from relational databases.