Online Structure Learning for Markov Logic Networks
Abstract
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.
Cite
Text
Huynh and Mooney. "Online Structure Learning for Markov Logic Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011. doi:10.1007/978-3-642-23783-6_6Markdown
[Huynh and Mooney. "Online Structure Learning for Markov Logic Networks." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011.](https://mlanthology.org/ecmlpkdd/2011/huynh2011ecmlpkdd-online/) doi:10.1007/978-3-642-23783-6_6BibTeX
@inproceedings{huynh2011ecmlpkdd-online,
title = {{Online Structure Learning for Markov Logic Networks}},
author = {Huynh, Tuyen N. and Mooney, Raymond J.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2011},
pages = {81-96},
doi = {10.1007/978-3-642-23783-6_6},
url = {https://mlanthology.org/ecmlpkdd/2011/huynh2011ecmlpkdd-online/}
}