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 top-down approach in three real-world domains.

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In Proceedings of 24th International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.

@InProceedings{mihalkova:icml07, title={Bottom-Up Learning of Markov Logic Network Structure}, author={Lilyana Mihalkova and Raymond J. Mooney}, booktitle={Proceedings of 24th International Conference on Machine Learning (ICML-2007)}, month={June}, address={Corvallis, OR}, url="http://www.cs.utexas.edu/users/ai-lab?mihalkova:icml07", year={2007} }