---
title: "Generative Structure Learning for Markov Logic Networks Based on Graph of Predicates"
authors:
  - "Quang-Thang Dinh"
  - "Matthieu Exbrayat"
  - "Christel Vrain"
doi: "10.5591/978-1-57735-516-8/IJCAI11-212"
url: "https://ijcai.org/proceedings/2011/212"
pdf: "https://ijcai.org/proceedings/2011/0212.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "1249-1253"
track: ""
keywords:
---

# Generative Structure Learning for Markov Logic Networks Based on Graph of Predicates

Quang-Thang Dinh, Matthieu Exbrayat, Christel Vrain

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 1249-1253. DOI: [10.5591/978-1-57735-516-8/IJCAI11-212](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-212)

## 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.