Semantics and Knowledge Acquisition in Bayesian Knowledge-Bases
Eugene Santos, Eugene S. Santos, Solomon Eyal Shimony · 2002
Maintaining semantics for uncertainty is critical during knowledge acquisition. We examine Bayesian Knowledge-Bases (BKBs) which are a generalization of Bayesian net-works. BKBs provide a highly flexible and intuitive repre-sentation following a basic “if-then ” structure in conjunction with probability theory. We present theoretical results con-cerning BKBs and how BKBs naturally and implicitly pre-serve semantics as new knowledge is added. In particular, equivalence of rule weights and conditional probabilities is achieved through stability of inferencing in BKBs.