Speeding up inferences using relevance reasoning: a formalism and algorithms

摘要

Irrelevance reasoning refers to the process in which a system reasons about which parts of its knowledge are relevant (or irrelevant) to a specific query. Aside from its importance in speeding up inferences from large knowledge bases, relevance reasoning is crucial in advanced applications such as modeling complex physical devices and information gathering in distributed heterogeneous systems. This article presents a novel framework for studying the various kinds of irrelevance that arise in inference and efficient algorithms for relevance reasoning. We present a proof-theoretic framework for analyzing definitions of irrelevance. The framework makes the necessary distinctions between different notions of irrelevance that are important when using them for speeding up inferences. We describe the query-tree algorithm which is a sound, complete and efficient algorithm for automatically deriving certain kinds of irrelevance claims for Horn-rule knowledge bases and several extensions. Finally, we describe experimental results that show that significant speedups (often orders of magnitude) are obtained by employing the query-tree in inference.