Semantics and efficient compilation for quantitative deductive databases

A coherent approach is presented that extends relational and deductive database technology toward an integration of expert-system applications, which require sound and efficient capabilities to deal with uncertainty. Extending logic programming, the authors define the semantics of quantitative deductive databases, where fixpoint theory plays a central role. Calculus gives the rule programmer a great deal of flexibility to tailor the aggregation of certainties according to the application expertise at hand. Extending relational algebra, the authors also introduce a quantitative relational algebra as a suitable target language for rule compilation. This approach makes rule-based expert systems requiring uncertainty reasoning on large and complex data, feasible for a variety of practical application areas.<>