Online Structure Learning for Markov Logic Networks

produced by: Data & Web Mining Lab
author: Raymond J. Mooney, Department of Computer Science, University of Texas at Austin
published: Nov. 29, 2011,   recorded: September 2011,   views: 3311


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


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 realworld datasets for natural-language field segmentation show that OSL outperforms systems that cannot revise structure.

See Also:

Download slides icon Download slides: ecmlpkdd2011_mooney_networks_01.pdf (402.8┬áKB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: