Ontology Enhancement through Inductive Decision Trees

The popularity of ontologies for representing the semantics behind many real-world domains has created a growing pool of ontologies on various topics. Different ontologists, experts, and organizations create a great variety of ontologies, often for narrow application domains. Some of the created ontologies frequently overlap with other ontologies in broader domains if they pertain to the Semantic Web. Sometimes, they model similar or matching theories that may be inconsistent. To assist in the reuse of these ontologies, this paper describes a technique for enriching manually created ontologies by supplementing them with inductively derived rules, and reducing the number of inconsistencies. The derived rules are translated from decision trees with probability measures, created by executing a tree based data mining algorithm over the data being modelled. These rules can be used to revise an ontology in order to extend the ontology with definitions grounded in empirical data, and identify possible similarities between complementary ontologies. We demonstrate the application of our technique by presenting an example, and discuss how various data types may be treated to generalize the semantics of an ontology for a broader application domain.