Merging Locally Correct Knowledge Bases: A Preliminary Report

TL;DR

This paper explores belief integration methods for merging knowledge bases with minimal change, proposing a generalized belief revision model that accounts for various types of mistakes causing inconsistency.

Contribution

It introduces a broader belief revision framework that extends beyond existing strategies, addressing different kinds of mistakes in knowledge bases.

Findings

Reformulation of the minimal change principle in belief revision terms

Discussion of alternative belief revision strategies

Insight into handling diverse mistakes in knowledge base merging

Abstract

Belief integration methods are often aimed at deriving a single and consistent knowledge base that retains as much as possible of the knowledge bases to integrate. The rationale behind this approach is the minimal change principle: the result of the integration process should differ as less as possible from the knowledge bases to integrate. We show that this principle can be reformulated in terms of a more general model of belief revision, based on the assumption that inconsistency is due to the mistakes the knowledge bases contain. Current belief revision strategies are based on a specific kind of mistakes, which however does not include all possible ones. Some alternative possibilities are discussed.