We tackle the problem of learning ontologies expressed in a rich representation like the ALC logic. This task can be cast as a supervised learning problem to be solved by means of operators for this representation which take into account the available metadata. The properties of such operators are discussed and their effectiveness is empirically tested in the experimentation reported in this paper.

A Counterfactual-Based Learning Algorithm for ALC Description Logic

ESPOSITO, Floriana;FANIZZI, Nicola;SEMERARO, Giovanni
2005

Abstract

We tackle the problem of learning ontologies expressed in a rich representation like the ALC logic. This task can be cast as a supervised learning problem to be solved by means of operators for this representation which take into account the available metadata. The properties of such operators are discussed and their effectiveness is empirically tested in the experimentation reported in this paper.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/114618
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