While methods to perform reasoning over such structures exist (Verhagen et al., 2007(Verhagen et al., , 2010UzZaman et al., 2013;Llorens et al., 2015;Pustejovsky et al., 2003), none of them have exploited advances in deep learning (Krizhevsky et al., 2012;Goodfellow et al., 2016).
recently proposed Graph Convolution Networks (GCN) (Defferrard et al., 2016;Kipf and Welling, 2017) have emerged as a way to learn graph representation while encoding structural information and constraints represented by the graph.