Coherent Integration of Databases by Abductive Logic Programming

TL;DR

This paper presents an abductive logic programming approach for integrating multiple databases coherently, ensuring consistency by identifying minimal data modifications through a solver and model-theoretic analysis.

Contribution

It introduces a novel abductive method and solver for database integration, with a formal model-theoretic foundation, surpassing existing methods in expressiveness.

Findings

The method effectively restores database consistency.

The approach is sound and complete with respect to a preferential semantics.

It is more expressive than previous integration techniques.

Abstract

We introduce an abductive method for a coherent integration of independent data-sources. 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 Asystem, 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.