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The convergence of embedded sensor systems and stream query processing suggests an important role for database techniques, in managing data that only partially - and often inaccurately - capture the state of the world. Reasoning about uncertainly as a first class citizen, inside a database system, becomes an increasingly important operation for processing non deterministic data. An essential step for such an approach lies in the choice of the appropriate uncertainty model, that captures the probabilistic information in the data, both accurately and at the right semantic detail level. This paper introduces Hierarchical First-Order Graphical Models (HVGMs), an intuitive and economical representation of the data correlations stored in a Probabilistic Data Management system, in a hierarchical setting. HFGM semantics allow for an efficient summarization of the probabilistic model that can be induced from a dataset at various levels of granularity, effectively controlling the trade-off of the model's complexity vs its accuracy.