A Paraconsistent Tableau Algorithm Based on Sign Transformation in Semantic Web
This work addresses the issue of handling noisy and inaccurate knowledge sources in the Semantic Web, though it appears incremental as it builds on existing paraconsistent approaches.
The paper tackles the problem of reasoning with inconsistent or incomplete data in the Semantic Web by proposing a paraconsistent tableau algorithm based on sign transformation, which is proven to be decidable and maintains the same functionality as classical tableau algorithms for consistent knowledge bases.
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.