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.
The problem of integrating information from conflicting sources comes up in many current applications, such as cooperative information systems, heterogeneous databases, and multiagent systems. We model this by the operation of merging first-order theories. We propose a formal semantics for this operation and show that it has desirable properties, including abiding by majority rule in case of conflict and syntax independence. We apply our semantics to the special case when the theories to be merged represent relational databases under integrity constraints. We then present a way of merging databases that have different or conflicting schemas caused by problems such as synonyms, homonyms or type conflicts mentioned in the schema integration literature. This paper introduces techniques for updating knowledge bases represented in extended logic programs. Three different types of updates, view updates, theory updates, and inconsistency removal, are considered. We formulate these updates through abduction, and provide methods for computing them with update programs. An update program is an extended logic program which specifies changes on abductive hypotheses, then updates are computed by the U-minimal answer sets of an update program. The proposed technique provides a uniform framework for these different types of updates, and each update is computed using existing procedures of logic programming. The integration of knowledge for multiple sources is an importantaspect of automated reasoning systems. When different knowledge basesare used to store knowledge provided by multiple sources, we are facedwith the problem of integrating multiple knowledge bases: Under thesecircumstances, we are also confronted with the prospect ofinconsistency. In this paper we present a uniform theoretical framework,based on annotated logics , foramalgamating multiple knowledge bases when these knowledge bases(possibly) contain inconsistencies, uncertainties, and nonmonotonicmodes of negation. We show that annotated logics may be used, with somemodifications, to mediate betweendifferent knowledge bases. The multiple knowledge bases are amalgamatedby a transformation of the individual knowledge bases into new annotatedlogic programs, together with the addition of a new axiom scheme. Wecharacterize the declarative semantics of such amalgamated knowledgebases and study how the semantics of the amalgam is related to thesemantics of the individual knowledge bases being combined. —Author's Abstract  The process of integrating knowledge coming from different sources has been widely investigated in the literature. Three distinct conceptual approaches to this problem have been most succesful: belief revision, merging and update. In this paper we present a framework that integrates these three approaches. In the proposed framework all three operations can be performed. We provide an example that can only be solved by applying more than one single style of knowledge integration and, therefore, cannot be addressed by anyone of the approaches alone. The framework has been implemented, and the examples shown in this paper (as well as other examples from the belief revision literature) have been successfully tested. Katsuno and Mendelzon divide theory change, the problem of adding new information to a logical theory, into two types: revision and update. We propose a third type of theory change: arbitration. The key idea is the following: the new information is considered neither better nor worse than the old information represented by the logical theory. The new information is simply one voice against a set of others already incorporated into the logical theory. From this follows that arbitration should be commutative. First we define arbitration by a set of postulates and then describe a model-theoretic characterization of arbitration for the case of propositional logical theories. We also study weighted arbitration where different models of a theory can have different weights. We present an approach for drawing plausible conclusions from inconsistent and incomplete knowledge-bases, which may also be prioritized. Our method is based on a four-valued semantics that is particularly suitable for reasoning with uncertainty. Our inference mechanism is closely related to some other well-known formalisms for handling inconsistent data, such as reasoning with maximal consistent subsets and possibilistic logic. It is shown that the formalism presented here is nonmonotonic, paraconsistent, and is capable of managing ranked data without having the “drowning problem”. In this paper we describe a new approach to repairing violations of integrity constraints in relational databases with null values. By adopting basic concepts from model-based diagnosis, we show how simultaneous reasons for violations of (different) constraints can be determined. These reasons, represented as sets of facts, directly indicate possible repair actions that guarantee to remove the observed violations. Combining knowledge present in multiple knowledge base systems into a single knowledge base is discussed. A knowledge based system can be considered an extension of a deductive database in that it permits function symbols as part of the theory. Alternative knowledge bases that deal with the same subject matter are considered. The authors define the concept of combining knowledge present in a set of knowledge bases and present algorithms to maximally combine them so that the combination is consistent with respect to the integrity constraints associated with the knowledge bases. For this, the authors define the concept of maximality and prove that the algorithms presented combine the knowledge bases to generate a maximal theory. The authors also discuss the relationships between combining multiple knowledge bases and the view update problem. >     Abstract During the process of knowledge acquisition from different experts it is usual that contradictions occur. Therefore strategies are needed for dealing with divergent statements and conflicts. We provide a formal framework to represent, process and combine distributed knowledge. The representation formalism is many-valued logic, which is a widely accepted method for expressing uncertainty, vagueness, contradictions and lack of information. Combining knowledge as proposed here makes use of the bilattice approach, which turns out to be very flexible and suggestive in the context of combining divergent information. We give some guidelines for choosing truth value spaces, assigning truth values and defining global operators to encode integration strategies. We describe the theory and implementation of a general theorem-proving technique for checking integrity of deductive databases recently proposed by Sadri and Kowalski. The method uses an extension of the SLDNF proof procedure and achieves the effect of the simplification algorithms of Nicolas, Lloyd, , and Decker by reasoning forwards from the update and thus focusing on the relevant parts of the database and the relevant constraints. This paper proposes a method of nonmonotonic theory change. We first introduce a new form of abduction that can account for observations in nonmonotonic situation. Then we provide a framework of autoepistemic update, which describes nonmonotonic theory change through the extended abductive framework. The proposed update semantics is fairly general and provides a unified framework for various update semantics such as first-order update, view update of databases, and contradiction removal of nonmonotonic theories.