On the Effectiveness of Evidence-Based Terminological Decision Trees

Abstract

Concept learning methods for Web ontologies inspired by Inductive Logic Programming and the derived inductive models for class-membership prediction have been shown to offer viable solutions to concept approximation, query answering and ontology completion problems. They generally produce human-comprehensible logic-based models (e.g. terminological decision trees) that can be checked by domain experts. However, one difficulty with these models is their inability to provide a way to measure the degree of uncertainty of the predictions. A framework for inducing terminological decision trees extended with evidential reasoning has been proposed to cope with these problems, but it was observed that the prediction procedure for these models tends to favor cautious predictions. To overcome this limitation, we further improved the algorithms for inducing/predicting with such models. The empirical evaluation shows promising results also in comparison with major related methods.