Rules Driven Object-Relational Databases Ontology Learning

A rules driven ontology learning approach from object-relational databases (ORDB) is proposed in this paper. The method extracts ontology from the Object-Relational Databases based on the Semantic Objects (SO) platform. And the core of this method aims at proposing a set of rules to obtain the basic elements which Web Ontology Language (OWL) needs. Based on the rules, the RDOL model is proposed. The model is driven by the rules to generate ontology. Our proposed approach reduces about forty-two percent learning time and obtains about eighteen percent higher performance comparing to the ontology learning from Relational databases (RDB).