## Speeding up Inference in Statistical Relational Learning by
Clustering Similar Query Literals

Lily Mihalkova
and
Matthew Richardson
### Abstract:

Markov logic networks (MLNs) are a statistical relational learning model
that consists of a set of weighted first-order clauses and provides a way
of softening first-order logic. Several machine learning problems have
been successfully addressed by treating MLNs as a “programming language”
where a set of features expressed in first-order logic is manually
engineered by the designer and then weights for these features are learned
from the data. Inference over the learned model is an important step in this
process both because several weight-learning algorithms involve performing
inference multiple times during training and because inference is used
to evaluate and use the final model. “Programming” with an MLN would
therefore be significantly facilitated by speeding up inference, thus providing
the ability to quickly observe the performance of new hand-coded
features. This paper presents a meta-inference algorithm that can speed up
any of the available inference techniques by first clustering the query literals
and then performing full inference for only one representative from
each cluster. Our approach to clustering the literals does not depend on
the weights of the clauses in the model. Thus, when learning weights for
a fixed set of clauses, the clustering step incurs only a one-time up-front
cost.
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