Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/979
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dc.contributor.authorWong, Tak Lamen_US
dc.date.accessioned2021-07-19T08:15:09Z-
dc.date.available2021-07-19T08:15:09Z-
dc.date.issued2014-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/979-
dc.description.abstractMarkov Logic Networks (MLN) is a unified framework integrating first-order logic and probabilistic inference. Most existing methods of MLN learning are supervised approaches requiring a large amount of training examples, leading to a substantial amount of human effort for preparing these training examples. To reduce such human effort, we have developed a semi-supervised framework for learning an MLN, in particular structure learning of MLN, from a set of unlabeled data and a limited number of labeled training examples. To achieve this, we aim at maximizing the expected pseudo-log-likelihood function of the observation from the set of unlabeled data, instead of maximizing the pseudo-log-likelihood function of the labeled training examples, which is commonly used in supervised learning of MLN. To evaluate our proposed method, we have conducted experiments on two different datasets and the empirical results demonstrate that our framework is effective, outperforming existing approach which considers labeled training examples alone.en_US
dc.language.isoenen_US
dc.publisherIOS Pressen_US
dc.relation.ispartofInternational Journal of Knowledge-Based and Intelligent Engineering Systemsen_US
dc.titleLearning Markov logic networks with limited number of labeled training examplesen_US
dc.typejournal articleen_US
dc.identifier.doi10.3233/KES-140289-
dc.contributor.affiliationSchool of Computing and Information Sciencesen_US
dc.relation.issn1875-8827en_US
dc.description.volume18en_US
dc.description.issue2en_US
dc.description.startpage91en_US
dc.description.endpage98en_US
dc.cihe.affiliatedYes-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.grantfulltextopen-
item.openairetypejournal article-
crisitem.author.deptYam Pak Charitable Foundation School of Computing and Information Sciences-
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