Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which becomes computationally expensive and eventually infeasible for large datasets with thousands of training examples which may not even all fit in main memory. To address this issue, previous work has used online learning to train MLNs. However, they all assume that the model's structure (set of logical clauses) is given, and only learn the model's parameters. However, the input structure is usually incomplete, so it should also be updated. In this work, we present OSL-the first algorithm that performs both online structure and parameter learning for MLNs. Experimental results on two real- world datasets for natural-language field segmentation show that OSL outperforms systems that cannot revise structure.

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In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), Vol. 2, pp. 81-96, September 2011.

@inproceedings{huynh:ecml11, title={Online Structure Learning for Markov Logic Networks}, author={Tuyen N. Huynh and Raymond J. Mooney}, booktitle={Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011)}, volume={2}, month={September}, pages={81-96}, url="http://www.cs.utexas.edu/users/ai-lab?huynh:ecml11", year={2011} }

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