Interval-based Possibilistic Description Logic Programs for the Semantic Web

Abstract

Integration ontologies and rules has become a central topic in the Semantic Web. In order to deal with uncertainty and inconsistent information, possibilistic description logic program has been investigated in recent years. However, possibilistic description logic program also cannot well model a great deal of real-world problems, because the accurate degrees associated with axioms and atoms are usually difficult to provide for experts. To address this problem, we further extend possibilistic description logic programs so that they can deal with inaccurate degrees associated with axioms and atoms. Therefore, we propose tightly coupled interval-based possibilistic description logic programs under possibilistic answer set semantics, which are a tight integration of disjunctive logic programs, interval-based possibilistic logics and possibilistic description logics. First of all, we define the syntax and semantics. Then, we show some semantic properties. Furthermore, we present three reasoning problems, and present some algorithms to solve these reasoning problems.