In this paper we consider two points of views to the problem of coherent integration of distributed data. First we give a pure model-theoretic analysis of the possible ways to repair' a database. We do so by characterizing the possibilities to recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. Then we introduce an abductive application to restore the consistency of a given database. This application is based on an abductive solver (A-system) that implements an SLDNFA-resolution procedure, and computes a list of data-facts that should be inserted to the database or retracted from it in order to keep the database consistent. The two approaches for coherent data integration are related by soundness and completeness results.
 When integrating data coming from multiple different sources we are faced with the possibility of inconsistency in databases. In this paper, we use one of the paraconsistent logics introduced in [9,7] (LFI1) as a logical framework to model possibly inconsistent database instances obtained by integrating different sources. We propose a method based on the sound and complete tableau proof system of LFI1 to treat both the integration process and the evolution of the integrated database submitted to users updates. In order to treat the integrated database evolution, we introduce a kind of generalized database context, the evolutionary databases, which are databases having the capability of storing and manipulating inconsistent information and, at the same time, allowing integrity constraints to change in time. We argue that our approach is sufficiently general and can be applied in most circumstances where inconsistency may arise in databases. The use of conventional classical logic is misleading for characterizing the behavior of logic programs because a logic program, when queried, will do one of three things: succeed with the query, fail with it, or not respond because it has fallen into infinite backtracking. In [7] Kleene proposed a three-valued logic for use in recursive function theory. The so-called third truth value was really undefined: truth value not determined. This logic is a useful tool in logic-program specification, and in particular, for describing models. (See [11].) Tarski showed that formal languages, like arithmetic, cannot contain their own truth predicate because one could then construct a paradoxical sentence that effectively asserts its own falsehood. Natural languages do allow the use of "is true", so by Tarski's argument a semantics for natural language must leave truth-value gaps: some sentences must fail to have a truth value. In [8] Kripke showed how a model having truth-value gaps, using Kleene's three-valued logic, could be specified. The mechanism he used is a famiUar one in program semantics: consider the least fixed point of a certain monotone operator. But that operator must be defined on a space involving three-valued logic, and for Kripke's application it will not be continuous. We apply techniques similar to Kripke's to logic programs. We associate with each program a monotone operator on a space of three-valued logic interpretations, or better partial interpretations. This space is not a complete lattice, and the operators are not, in general, continuous. But least and other fixed points do exist. These fixed points are shown to provide suitable three-valued program models. They relate closely to the least and greatest fixed points of the operators used in [1]. Because of the extra machinery involved, our treatment allows for a natural consideration of negation, and indeed, of the other prepositional connectives as well. And because of the elaborate structure of fixed points available, we are able to A general logic program (abbreviated to "program" hereafter) is a set of roles that have both positive and negative subgoals. It is common to view a deductive database as a general logic program consisting of rules (IDB) slttmg above elementary relations (EDB, facts). It is desirable to associate one Herbrand model with a program and think of that model as the "meaning of the program, " or Its "declarative semantics. " Ideally, queries directed to the program would be answered in accordance with this model. Recent research indicates that some programs do not have a "satisfactory" total model; for such programs, the question of an appropriate partial model arises. Unfounded sets and well-founded partial models are introduced and the well-founded semantics of a program are defined to be its well-founded partial model. If the well-founded partial model is m fact a total model. it is called the well-founded model. It n shown that the class of programs possessing a total well-founded model properly includes previously studied classes of "stratified" and "locally stratified" programs, The method in this paper is also compared with other proposals in the literature, including Clark's "program completion, " Fitting's and Kunen's 3-vahred interpretations of it, and the "stable models" of Gelfond and Lifschitz. The logics of formal inconsistency (LFI’s) are logics that allow to explicitly formalize the concepts of consistency and inconsistency by means of formulas of their language. Contradictoriness, on the other hand, can always be expressed in any logic, provided its language includes a symbol for negation. Besides being able to represent the distinction between contradiction and inconsistency, LFI’s are non-explosive logics, in the sense that a contradiction does not entail arbitrary statements, but yet are gently explosive, in the sense that, adjoining the additional requirement of consistency, then contradictoriness do cause explosion. Several logics can be seen as LFI’s, among them the great majority of paraconsistent systems developed under the Brazilian and Polish tradition. We present here tableau systems for some important LFI’s: bC, Ci and LFI1. The goal of this paper is to extend classical logic with a generalized notion of inductive definition supporting positive and negative induction, to investigate the properties of this logic, its relationships to other logics in the area of non-monotonic reasoning, logic programming and deductive databases, and to show its application for knowledge representation by giving a typology of definitional knowledge.