Ensemble Methods for Ontology Learning - An Empirical Experiment to Evaluate Combinations of Concept Acquisition Techniques

Most approaches to ontology learning combine techniques from different areas (hybrid approaches) to increase the efficiency of the ontology learning process. However, the results from the ontology learning process do not fully satisfy the users at present. An important problem is that there is a lack of quantitative and comparative data about the efficiency of techniques and technique combinations applied to ontology learning. In this paper we present a quantitative comparison of technique combinations for concept extraction and a software system (OntoLancs) to support the evaluation of techniques. By applying OntoLancs, users are able to assist the process of building ontologies by semi- automatically acquiring concepts from large-scale domain document collections and experiment with different combinations of knowledge acquisition techniques to refine and organize domain concepts into a taxonomy. Quantitative and comparative studies about the performance of several techniques and user experiences indicate the applicability and usefulness of our approach.