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

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Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1249-1253.

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.

Quang-Thang Dinh, Matthieu Exbrayat, and Christel Vrain. 2011. Generative Structure Learning for Markov Logic Networks Based on Graph of Predicates. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11. IJCAI, 1249–1253. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-212
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