Graph-based Approximate Counting for Relational Probabilistic Models

One of the key operations inside most relational probabilistic models is counting be it for parameter/structure learning or for efficient inference. However, most approaches use the logical structure for counting and do not exploit any fast counting methods. In this work-inprogress, we explore the closer connections to graph data bases and propose methods that obtain both exact and approximate counts effectively. We demonstrate the efficiency in the task of parameter learning.

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