Relational databases are valuable sources for ontology learning. Methods and tools have been proposed to generate ontologies from such structured input. However, a major persisting limitation is the derivation of ontologies with flat structure that simply mirror the schema of the source databases. In this paper, we show how the RDBToOnto tool can be used to derive accurate ontologies by taking advantage of both the database schema and the data, and more specifically through identification of taxonomies hidden in the data. This extensible tool supports an iterative approach that allows progressive refinement of the learning process through user-defined constraints.
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