Improving Markov Logic Network learning using unlabeled data

Tak Lam Wong, Kai On Chow, Fu Lee Wang, Pilllip M. Tsang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages236-240
Number of pages5
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
Duration: 11 Jul 201014 Jul 2010

Publication series

Name2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume1

Conference

Conference2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Country/TerritoryChina
CityQingdao
Period11/07/1014/07/10

Keywords

  • MLN
  • Markov logic networks
  • Semi-supervised learning

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