Generative Structure Learning for Markov Logic Networks Based on Graph of Predicates
Abstract
In this paper we present a new algorithm for generatively learning the structure of Markov Logic Networks. This algorithm relies on a graph of predicates, which summarizes the links existing between predicates and on relational information between ground atoms in the training database. Candidate clauses are produced by means of a heuristical variabilization technique. According to our first experiments, this approach appears to be promising.
Cite
Text
Dinh et al. "Generative Structure Learning for Markov Logic Networks Based on Graph of Predicates." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-212Markdown
[Dinh et al. "Generative Structure Learning for Markov Logic Networks Based on Graph of Predicates." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/dinh2011ijcai-generative/) doi:10.5591/978-1-57735-516-8/IJCAI11-212BibTeX
@inproceedings{dinh2011ijcai-generative,
title = {{Generative Structure Learning for Markov Logic Networks Based on Graph of Predicates}},
author = {Dinh, Quang-Thang and Exbrayat, Matthieu and Vrain, Christel},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {1249-1254},
doi = {10.5591/978-1-57735-516-8/IJCAI11-212},
url = {https://mlanthology.org/ijcai/2011/dinh2011ijcai-generative/}
}