Neural networks for relational learning: an experimental comparison

By Werner Uwents, Gabriele Monfardini, Hendrik Blockeel, Marco Gori, Franco Scarselli. 2011

39 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Filip Zelezný
2. Ondřej Kuželka
3. Steven Schockaert
4. Gustav Sourek
5. Vojtech Aschenbrenner

In Statistical and Nonlinear Physics

1. Filip Zelezný
2. Ondřej Kuželka
3. Steven Schockaert
4. Gustav Sourek
5. Vojtech Aschenbrenner

Citations in the corpus

• Gated Graph Sequence Neural Networks (2015, neutral): “GNNs have been applied in several domains (Gori et al., 2005; Di Massa et al., 2006; Scarselli et al., 2009; Uwents et al., 2011), but they do not appear to be in widespread use in the ICLR community.”
• Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures (2018, background): “There have been a few other approaches which adapt neural networks for relational learning (Franca, Zaverucha, & Garcez, 2014; Botta, A, & Piola, 1997; Bader & Hitzler, 2005; Ramon & De Raedt, 2000; Uwents, Monfardini, Blockeel, Gori, & Scarselli, 2011).”
• Fast relational learning using bottom clause propositionalization with artificial neural networks (2013, unknown): “In neural-symbolic integration, the representation of first-order logic by neural networks is of interest in its own right, since first-order logic learning and reasoning using connectionist systems remains an open research question (Garcez et al., 2008).”