Bottom-Up Learning of Markov Logic Network Structure
Lily Mihalkova
and
Raymond Mooney
Abstract:
Markov logic networks (MLNs) are a statistical
relational model that consists of weighted first-order
clauses and generalizes first-order logic
and Markov networks. The current state-of-the-art
algorithm for learning MLN structure follows
a top-down paradigm where many potential
candidate structures are systematically generated
without considering the data and then evaluated
using a statistical measure of their fit to
the data. Even though this existing algorithm
outperforms an impressive array of benchmarks,
its greedy search is susceptible to local maxima
or plateaus. We present a novel algorithm
for learning MLN structure that follows a more
bottom-up approach to address this problem. Our
algorithm uses a .propositional. Markov network
learning method to construct .template.
networks that guide the construction of candidate
clauses. Our algorithm significantly improves
accuracy and learning time over the existing topdown
approach in three real-world domains.
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Datasets used:
IMDB
UW-CSE