This paper describes
foil, a system that learns Horn clauses from data expressed as relations.
foil is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.
Keywords Induction - first-order rules - relational data - empirical learning