Toward Effective Knowledge Acquisition with First Order Logic Induction

Given a set of noisy training examples, an approximate theory probably including multiple predicates and background knowledge, to acquire a more accurate theory is a more realistic problem in knowledge acquisition with machine learning methods. An algorithm called KNOWAR[2] that combines a multiple predicate learning module and a theory revision module has been used to deal with such a problem, where inductive logic programming methods are employed