MALCHIODI , Dario

We address the problem of predicting a score for candidate axioms within the context of ontology learning. The prediction is based on a learning procedure based on support vector clustering originally developed for inferring the membership functions of fuzzy sets, and on a similarity measure for subsumption axioms based on semantic...

Although programming is often seen as a key element of constructionist approaches, the research on learning to program through a constructionist strategy is somewhat limited, mostly focusing on how to bring the abstract and formal nature of programming languages into "concrete", possibly tangible objects, graspable even by children with limited...

Although programming is often seen as a key element of constructionist approaches, the research on learning to program through a constructionist strategy is somewhat limited, mostly focusing on how to bring the abstract and formal nature of programming languages into "concrete" or even tangible objects, graspable even by children with limited...

We assess the role of similarity measures and learning methods in classifying candidate axioms for automated schema induction through kernel-based learning algorithms. The evaluation is based on (i) three different similarity measures between axioms, and (ii) two alternative dimensionality reduction techniques to check the extent to which the...

Within the context of ontology learning, we consider the problem of selecting candidate axioms through a suitable score. Focusing on subsumption axioms, this score is learned coupling support vector regression with a special similarity measure inspired by the Jaccard index and justified by semantic considerations. We show preliminary results...