Counting the number of true instances of a clause is arguably a major bottleneck in relational probabilistic inference and learning. We approximate counts in two steps:(1) transform the fully grounded relational model to a large hypergraph, and partially-instantiated clauses to hypergraph motifs;(2) since the expected counts of the motifs are provably the clause counts, approximate them using summary statistics (in\/outdegrees, edge counts, etc). Our experimental results demonstrate the efficiency of these approximations, which can be applied to many complex statistical relational models, and can be significantly faster than state-of-the-art, both for inference and learning, without sacrificing effectiveness.<\/p>\n","protected":false},"excerpt":{"rendered":"
Counting the number of true instances of a clause is arguably a major bottleneck in relational probabilistic inference and learning. We approximate counts in two steps:(1) transform the fully grounded relational model to a large hypergraph, and partially-instantiated clauses to hypergraph motifs;(2) since the expected counts of the motifs are provably the clause counts, approximate 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