Knowledge reasoning and Tableau Algorithm improving based on rough description logics

Abstract

The knowledge base of description logics are composed of two parts TBox and ABox. Tableau Algorithm is for the uniformity testing in the knowledge reasoning of DLs, which is based on two-value logics, it can not realize the uniformity testing for multiple-valued concepts. This paper take the fundamental ideal to the system of DLs, improving Tableau Algorithm through the definition of rough concept implication degree, and by using rough concept express related concepts and relationships in the TBbox, The rough description logics can be completed on the reasoning of rough concept, laying the foundation for knowledge base inference engine design.