Showing papers by "Ofer Arieli published in 2004"

Coherent integration of databases by abductive logic programming

Abstract: We introduce an abductive method for a coherent integration of independent datasources. The idea is to compute a list of data-facts that should be inserted to the amalgamated database or retracted from it in order to restore its consistency. This method is implemented by an abductive solver, called A system, that applies SLDNFA-resolution on a meta-theory that relates different, possibly contradicting, input databases. We also give a pure model-theoretic analysis of the possible ways 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. This allows us to characterize the 'recovered databases' in terms of the 'preferred' (i.e., most consistent) models of the theory. The outcome is an abductive-based application that is sound and complete with respect to a corresponding model-based, preferential semantics, and - to the best of our knowledge - is more expressive (thus more general) than any other implementation of coherent integration of databases.

Database Repair by Signed Formulae

Abstract: We introduce a simple and practically efficient method for repairing inconsistent databases. The idea is to properly represent the underlying problem, and then use off-the-shelf applications for efficiently computing the corresponding solutions.

Relating intuitionistic fuzzy sets and interval-valued fuzzy sets through bilattices

Abstract: In this paper, we show that bilattices are robust mathematical structures that provide a natural accommodation to, and bridge between, intuitionistic fuzzy sets and interval-valued fuzzy sets. In this way, we resolve the controversy surrounding the formal equivalence of these two models, and open up the path for a new tradition for representing positive and negative information in fuzzy set theory. 1. Motivation Bilattices are algebraic structures that were introduced by Ginsberg 15 , and further examined by Fitting 11,12,13 and others 1,2,14 , as a general framework for many applications in computer science. In this paper, we show that these structures can also elegantly and naturally accommodate intuitionistic fuzzy sets (IFSs) and interval-valued fuzzy sets (IVFSs), which are two frequently encountered and syntactically equivalent generalizations of Zadeh’s fuzzy sets. In particular, and more generally than in previous works, we demonstrate that Atanassov’s decision to restrict the evaluation set for L-intuitionistic fuzzy sets to consistent couples of the “square” L 2 forces the resulting structure to coincide with the “triangle” I(L). This insight provides a convenient stepping stone towards more general and expressive models for the representation and processing of positive and negative imprecise information.

Paraconsistent preferential reasoning by signed quantified Boolean formulae

Abstract: We introduce a uniform approach of representing a variety of paraconsistent nonmonotonic formalisms by quantified Boolean formulae (QBFs) in the context of four-valued semantics. This framework provides a useful platform for capturing, in a simple and natural way, a wide range of methods for preferential reasoning. Off-the-shelf QBF solvers may therefore be incorporated for simulating the corresponding consequence relations.

Data Integration Using ID-Logic

Abstract: ID-Logic is a knowledge representation language that extends first-order logic with non-monotone inductive definitions. This paper introduces an ID-Logic based framework for database schema integration. It allows us to to uniformly represent and reason with independent source databases that contain information about a common domain, but may have different schemas. The ID-Logic theories that are obtained are called mediator-based systems. We show that these theories properly capture the common methods for data integration (i.e., global-as view and local-as-view with either exact or partial definitions), and apply on them a robust abductive inference technique for query answering.