Answering Queries from Context­Sensitive Probabilistic Knowledge Bases

Liem Ngo and Peter Haddawy Department of Electrical Engineering and Computer Science University of Wisconsin­Milwaukee PO Box 784 Milwaukee, WI 53201


We define a language for representing context­sensitive probabilistic knowledge. A knowledge base consists of a set of universally quantified probability sentences that include context constraints, which allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a query answering procedure which takes a query Q and a set of evidence E and constructs a Bayesian network to compute P (QjE). The posterior probability is then computed using any of a number of Bayesian network inference algorithms. We use the declarative semantics to prove the query procedure sound and complete. We use concepts from logic programming to justify our approach.