## Efficient Weight Learning for Markov Logic Networks

Daniel Lowd
and
Pedro Domingos
### Abstract:

Markov logic networks (MLNs) combine Markov networks and first-order logic,
and are a powerful and increasingly popular representation
for statistical relational learning. The state-of-the-art method
for discriminative learning of MLN weights is the voted perceptron algorithm,
which is essentially gradient descent with an MPE approximation
to the expected sufficient statistics (true clause counts). Unfortunately,
these can vary widely between clauses, causing the learning problem to
be highly ill-conditioned, and making gradient descent very slow. In this
paper, we explore several alternatives, from per-weight learning rates
to second-order methods. In particular, we focus on two approaches that
avoid computing the partition function: diagonal Newton and scaled conjugate
gradient. In experiments on standard SRL datasets, we obtain
order-of-magnitude speedups, or more accurate models given comparable
learning times.
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### Datasets used:

Cora

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