Qualitative probabilistic relational models

Abstract

Probabilistic relational models (PRMs) were introduced to extend the modelling and reasoning capacities of Bayesian networks from propositional to relational domains. PRMs are typically learned from relational data, by extracting from these data both a dependency structure and its numerical parameters. For this purpose, a large and rich data set is required, which proves prohibitive for many real-world applications. Since a PRM’s structure can often be readily elicited from domain experts, we propose manual construction by an approach that combines qualitative concepts adapted from qualitative probabilistic networks (QPNs) with stepwise quantification. To this end, we introduce qualitative probabilistic relational models (QPRMs) and tailor an existing algorithm for qualitative probabilistic inference to these new models.