Learning Probabilistic Description Logic Concepts Under Alternative Assumptions on Incompleteness

Real-world knowledge often involves various degrees of uncertainty. For such a reason, in the Semantic Web context, difficulties arise when modeling real-world domains using only purely logical formalisms. Alternative approaches almost always assume the availability of probabilistically-enriched knowledge, while this is hardly known in advance. In addition, purely deductive exact inference may be infeasible for Web-scale ontological knowledge bases, and does not exploit statistical regularities in data. Approximate deductive and inductive inferences were proposed to alleviate such problems. This article proposes casting the concept-membership prediction problem (predicting whether an individual in a Description Logic knowledge base is a member of a concept) as estimating a conditional probability distribution which models the posterior probability of the aforementioned individual’s concept-membership given the knowledge that can be entailed from the knowledge base regarding the individual. Specifically, we model such posterior probability distribution as a generative, discriminatively structured, Bayesian network, using the individual’s concept-membership w.r.t. a set of feature concepts standing for the available knowledge about such individual. Uncertainty Reasoning for the Semantic Web III Uncertainty Reasoning for the Semantic Web III Look Inside Other actions Reprints and Permissions Export citation About this Book Add to Papers Share Share this content on Facebook Share this content on Twitter Share this content on LinkedIn