Speeding Up Inference in Statistical Relational Learning by Clustering Similar Query Literals

Markov logic networks (MLNs) have been successfully applied to several challenging problems by taking a "programming language" approach where a set of formulas is hand-coded and weights are learned from data. Because inference plays an important role in this process, "programming" with an MLN would be significantly facilitated by speeding up inference. We present a new meta-inference algorithm that exploits the repeated structure frequently present in relational domains to speed up existing inference techniques. Our approach first clusters the query literals and then performs full inference for only one representative from each cluster. The clustering step incurs only a one-time up-front cost when weights are learned over a fixed structure.

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