Query Processing and Optimization for Logic Programs with Certainty Constraints

Numerous logic frameworks have been proposed for modeling uncertainty and reasoning with such data. While different in syntax, the approaches of these frameworks have been classified into "annotation based" (AB) and "implication based" (IB). In this paper, we present a unified framework which allows evaluating programs in either approach. It extends existing query processing techniques to handle certainty constraints and uses heuristics to further improve the performance. Our experiments indicate that the proposed techniques yield useful tools for uncertainty reasoning.