Learning the Parameters of Probabilistic Logic Programs from Interpretations
Abstract
ProbLog is a recently introduced probabilistic extension of the logic programming language Prolog, in which facts can be annotated with the probability that they hold. The advantage of this probabilistic language is that it naturally expresses a generative process over interpretations using a declarative model. Interpretations are relational descriptions or possible worlds. This paper introduces a novel parameter estimation algorithm LFI-ProbLog for learning ProbLog programs from partial interpretations. The algorithm is essentially a Soft-EM algorithm. It constructs a propositional logic formula for each interpretation that is used to estimate the marginals of the probabilistic parameters. The LFI-ProbLog algorithm has been experimentally evaluated on a number of data sets that justifies the approach and shows its effectiveness.
Cite
Text
Gutmann et al. "Learning the Parameters of Probabilistic Logic Programs from Interpretations." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011. doi:10.1007/978-3-642-23780-5_47Markdown
[Gutmann et al. "Learning the Parameters of Probabilistic Logic Programs from Interpretations." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011.](https://mlanthology.org/ecmlpkdd/2011/gutmann2011ecmlpkdd-learning/) doi:10.1007/978-3-642-23780-5_47BibTeX
@inproceedings{gutmann2011ecmlpkdd-learning,
title = {{Learning the Parameters of Probabilistic Logic Programs from Interpretations}},
author = {Gutmann, Bernd and Thon, Ingo and De Raedt, Luc},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2011},
pages = {581-596},
doi = {10.1007/978-3-642-23780-5_47},
url = {https://mlanthology.org/ecmlpkdd/2011/gutmann2011ecmlpkdd-learning/}
}