A Forgetting-based Approach to Merging Knowledge Bases

TL;DR

This paper introduces a new knowledge merging method based on variable forgetting, which filters variables to resolve conflicts and offers more intuitive insights compared to traditional model selection approaches.

Contribution

It establishes a relationship between belief merging and variable forgetting, and develops new merging operators by modifying candidate variables to improve traditional methods.

Findings

New merging operators based on variable forgetting are proposed.

The approach provides intuitive information about atom variables.

The method effectively resolves conflicts in knowledge bases.

Abstract

This paper presents a novel approach based on variable forgetting, which is a useful tool in resolving contradictory by filtering some given variables, to merging multiple knowledge bases. This paper first builds a relationship between belief merging and variable forgetting by using dilation. Variable forgetting is applied to capture belief merging operation. Finally, some new merging operators are developed by modifying candidate variables to amend the shortage of traditional merging operators. Different from model selection of traditional merging operators, as an alternative approach, variable selection in those new operators could provide intuitive information about an atom variable among whole knowledge bases.