According to ANSI[1], null values are known to have 14 different meanings. But, in general, they can be classified into two categories: unknown values and inapplicable values. This thesis proposes a new approach for solving the unknown value problems with Implicit Predicate (IP) in deductive database application environments. The IP serves as a descriptor corresponding to a set of the unknown values, thereby expressing their semantics. In this thesis, we demonstrate that the IP framework formalizing unknown value problems in the logical point of view is capable of 1) enhancing the semantic expressiveness of the unknown values, 2) entering incomplete information into database, 3) exploiting the information and a variety of inference rules in database to reduce the uncertainties of the unknown values and 4) preserving the properties of the relational operators with a slight modifications. Overall inference rule can be represented in terms of generalized dependency statements which are a kind of horn clauses. In this thesis we employ the functional dependency and the inclusion dependency as examples of the inference rules since the two dependency statements are the most frequently referenced inference rules in DB application because of their powerful expressiveness for the semantics of relational DB model. In addition, we also propose a query evaluation technique for IP to minimize indefiniteness of answers in the proof theoretic view. Conjunctive Normal Form(CNF) and Disjunctive Normal Form(DNF) of Lipski are adopted as the form processed in this thesis since they are considered as the most procedural form appropriate to our view among those forms ever proposed in deductive database application. After the procedure obtaining exact answers from CNF, and maybe answer from DNF is formalized, the proof is followed that it is sound and complete. We also show that procedure trying to obtaining exact answers from DNF, and maybe answers from CNF is not sound and complete...