2013/03/13 by Raymond T. Ng, Ng, Raymond T., V. S. Subrahmanian +1
Computer Science · #Advanced Database Systems and Queries #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Databases (cs.DB) #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #cs.AI #cs.DB
paper · pdf · doi:10.48550/arxiv.1303.5420
Appears in Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (UAI1992)
arxiv created 2013/03/13 · openalex publication_date 2013/03/13 · arxiv updated 2013/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We address the problem of supporting empirical probabilities in monadic logic databases. Though the semantics of multivalued logic programs has been studied extensively, the treatment of probabilities as results of statistical findings has not been studied in logic programming/deductive databases. We develop a model-theoretic characterization of logic databases that facilitates such a treatment. We present an algorithm for checking consistency of such databases and prove its total correctness. We develop a sound and complete query processing procedure for handling queries to such databases.