Gold standard based evaluation of ontology learning techniques

A growing attention has been paid to the ontology learning domain. This is due to its importance for overcoming the limits of manual ontology building. Thus, ontology evaluation becomes crucial and very much-needed in order to select the best performing ontology learning method. The aim of the present paper is to offer a new method for assessing a learned ontology in comparison to a gold standard one. In order to avoid issues of previous precision and recall measures, the proposed method is based on a new ontology disambiguation engine. The latter provides meaning annotations to concepts. Next, we propose a set of measures that exploits the meanings of concepts to evaluate the learned ontologies. To prove the efficiency of the proposed solution, we conduct a set of experiments that test our method on well-known ontologies. Experiments show that these measures scale gradually in the closed interval of[0;1]as learned ontologies deviate increasingly from the gold standard.