  A major problem in computational learning theory is whether the class of
formulas in conjunctive normal form (CNF) is efficiently learnable. Although it
is known that this class cannot be polynomially learned using either membership
or equivalence queries alone, it is open whether CNF can be polynomially
learned using both types of queries. One of the most important results
concerning a restriction of the class CNF is that propositional Horn formulas
are polynomial time learnable in Angluin's exact learning model with membership
and equivalence queries. In this work we push this boundary and show that the
class of multivalued dependency formulas (MVDF) is polynomially learnable from
interpretations. We then provide a notion of reduction between learning
problems in Angluin's model, showing that a transformation of the algorithm
suffices to efficiently learn multivalued database dependencies from data
relations. We also show via reductions that our main result extends well known
previous results and allows us to find alternative solutions for them.
