@inproceedings{ce0ac4fecd59494f885c91184dec2506,
title = "Improving Markov Logic Network learning using unlabeled data",
abstract = "Existing Markov Logic Network (MLN) learning methods aim at learning an MLN from a set of training examples. To reduce the human effort in preparing training examples, we have developed a semi-supervised framework for learning an MLN from unlabeled data and a limited number of training examples. One characteristic of our approach is that instead of maximizing the pseudo-log-likelihood function of the labeled training examples, we aim at optimizing the pseudo-Ioglikelihood function of the observation from the set of unlabeled data. The learned MLN can then be applied to the unlabeled data for conducting inference in a more precise manner. We have conducted experiments and the empirical results demonstrate that our framework is effective, outperforming existing approach which considers labeled training examples alone.",
keywords = "MLN, Markov logic networks, Semi-supervised learning",
author = "Wong, {Tak Lam} and Chow, {Kai On} and Wang, {Fu Lee} and Tsang, {Pilllip M.}",
year = "2010",
doi = "10.1109/ICMLC.2010.5581061",
language = "English",
isbn = "9781424465262",
series = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",
pages = "236--240",
booktitle = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",
note = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 ; Conference date: 11-07-2010 Through 14-07-2010",
}