An automatic algorithm for building ontologies from data

We describe an automatic algorithm able to learn university courses ontologies from experimental data. This algorithm is based on the use of the Bayesian networks formalism for representing ontologies, as well as on the use of a learning algorithm that infers the corresponding probabilistic model starting from the results final courses tests. According a multiexpert approach, this method uses Bayesian networks structural learning algorithms in order to build reference ontologies. This algorithm aims to help teachers in the organization of courses and students in the definition of customized learning path. We provide an experimental evaluation of the method using data coming from real courses.