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Issue No. 06 - December (1993 vol. 5)
ISSN: 1041-4347
pp: 939-949
<p>Most current applications of inductive learning in databases take place in the context of a single extensional relation. The authors place inductive learning in the context of a set of relations defined either extensionally or intentionally in the framework of deductive databases. LINUS, an inductive logic programming system that induces virtual relations from example positive and negative tuples and already defined relations in a deductive database, is presented. Based on the idea of transforming the problem of learning relations to attribute-value form, several attribute-value learning systems are incorporated. As the latter handle noisy data successfully, LINUS is able to learn relations from real-life noisy databases. The use of LINUS for learning virtual relations is illustrated, and a study of its performance on noisy data is presented.</p>
inductive learning; deductive databases; single extensional relation; LINUS; inductive logic programming system; virtual relations; negative tuples; attribute-value form; attribute-value learning systems; noisy data; real-life noisy databases; deductive DBMS; database theory; deductive databases; learning (artificial intelligence); logic programming

S. Dzeroski and N. Lavrac, "Inductive Learning in Deductive Databases," in IEEE Transactions on Knowledge & Data Engineering, vol. 5, no. , pp. 939-949, 1993.
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