Implementing Uncertainty in a Logic Programming Framework

Abstract. We briefly outline the need to incorporate uncertainty and flexibility into the semantic web knowledge representation, and argue that support logic programming- a combination of soft computing and logic programming – is one way to address this problem. To retain consistency with emerging frameworks, we show how a support logic program modelling uncertainty in relations and rules can be compiled into crisp Horn clauses suitable for reasoning within a system which does not explicitly model uncertainty. In cases where there is uncertainty in attribute values, an extension to the inference mechanism is required