Abstract
We tackle the problem of learning ontologies expressed in a rich representation like the \(\mathcal{ALC}\) 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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Esposito, F., Fanizzi, N., Iannone, L., Palmisano, I., Semeraro, G. (2005). A Counterfactual-Based Learning Algorithm for \(\mathcal{ALC}\) Description Logic. In: Bandini, S., Manzoni, S. (eds) AI*IA 2005: Advances in Artificial Intelligence. AI*IA 2005. Lecture Notes in Computer Science(), vol 3673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558590_41
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DOI: https://doi.org/10.1007/11558590_41
Publisher Name: Springer, Berlin, Heidelberg
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