A Paraconsistent Tableau Algorithm Based on Sign Transformation in Semantic Web

TL;DR

This paper introduces a paraconsistent tableau algorithm utilizing sign transformation to enhance reasoning capabilities in the Semantic Web, effectively handling inconsistent or incomplete data while maintaining decidability.

Contribution

It presents a novel tableau algorithm based on sign transformation that improves reasoning in the Semantic Web with inconsistent data, ensuring decidability.

Findings

The algorithm is decidable.

It retains classical tableau reasoning for consistent knowledge.

Enhanced ability to handle noisy, inconsistent data.

Abstract

In an open, constantly changing and collaborative environment like the forthcoming Semantic Web, it is reasonable to expect that knowledge sources will contain noise and inaccuracies. It is well known, as the logical foundation of the Semantic Web, description logic is lack of the ability of tolerating inconsistent or incomplete data. Recently, the ability of paraconsistent approaches in Semantic Web is weaker in this paper, we present a tableau algorithm based on sign transformation in Semantic Web which holds the stronger ability of reasoning. We prove that the tableau algorithm is decidable which hold the same function of classical tableau algorithm for consistent knowledge bases.