Bayesian knowledge-driven ontologies: Intuitive uncertainty reasoning for semantic networks

Uncertainty handling for semantic networks is a difficult problem which has slowed the effort to fully develop a semantic web. Uncertainty handling becomes particularly challenging when incompleteness is present in a domain, as it frequently is when modeling real-world complexity. To date, work on uncertainty frameworks for semantic networks has not intuitively captured a useful notion of uncertainty, for reasons including weaknesses in underlying uncertainty theories and assumption conflicts with semantic networks. We propose a framework which is a synthesis of semantic networks and Bayesian Knowledge Bases, which are a generalization of Bayesian Networks to accommodate incompleteness. This synthesis represents knowledge as “if-then” conditional probability rules between description logic assertions. We define simple methods for reasoning about semantic information under uncertainty and about uncertainty itself. Our results show potential to remove some obstructions in the path to a semantic web.