Reasoning about Belief Uncertainty in DL Lite N bool

ABSTRACT. Dealing with uncertainty is a very important issue in description logics (DLs). Therefore, several approaches proposed probabilistic extensions of DLs but most of them are difficult to use and don't support the belief degree in terminological axioms or use graphical models such as Bayesian Network or are based on classical models. In this paper, we present PrDL lite N bool a new probabilistic extension of DL Lite N bool, known as a very expressive DL Lite, by supporting the belief degree interval in a single axiom or a set of axioms connected with conjunction (by ∧) or disjunction (by ∨) operators. The PrDL lite N bool semantics is based on DL Lite N bool features which are a new alternative semantics for DL lite N bool having a finite structure and its number is always finite unlike classical models. PrDL lite N bool also supports terminological and assertional probabilistic knowledge and the reasoning tasks (satisfiability, deciding the entailment, computing tight interval for entailment) are achieved by solving linear optimization problems. A prototype of the approach is implemented using OWLAPI for knowledge base creation, Pellet for reasoning and LpSolve for solving the linear programs.