On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Machine Learning - special issue on inductive logic programming
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Logical and Relational Learning: From ILP to MRDM (Cognitive Technologies)
Statistical predicate invention
Proceedings of the 24th international conference on Machine learning
Bottom-up learning of Markov logic network structure
Proceedings of the 24th international conference on Machine learning
Discriminative structure and parameter learning for Markov logic networks
Proceedings of the 25th international conference on Machine learning
Efficient Weight Learning for Markov Logic Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Discriminative Structure Learning of Markov Logic Networks
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Learning Markov logic network structure via hypergraph lifting
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Structure Learning of Markov Logic Networks through Iterated Local Search
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Memory-efficient inference in relational domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Max-Margin Weight Learning for Markov Logic Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A general method for reducing the complexity of relational inference and its application to MCMC
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning relations by pathfinding
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Speeding up inference in statistical relational learning by clustering similar query literals
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Generative structure learning for Markov logic networks based on graph of predicates
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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In this paper, we present a generative algorithm to learn Markov Logic Network (MLN) structures automatically, directly from a training dataset. The algorithm follows a bottom-up approach by first heuristically transforming the training dataset into boolean tables, then creating candidate clauses using these boolean tables and finally choosing the best clauses to build the MLN. Comparisons to the state-of-the-art structure learning algorithms for MLNs in two real-world domains show that the proposed algorithm outperforms them in terms of the conditional log likelihood (CLL), and the area under the precision-recall curve (AUC).