Neural Relational Learning Through Semi-Propositionalization of Bottom Clauses
Manoel Vitor Macedo França, Gerson Zaverucha, Artur d’Avila Garcez · City Research Online (City University London) · 2015
Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis in social networks. The CILP++ system is a neural-symbolic system which can perform efficient relational learning, by being able to process first-order logic knowledge into a neural network. CILP++ relies on BCP, a recently discovered propositionalization algorithm, to perform relational learning. However, efficient knowledge extraction from such networks is an open issue and features generated by BCP do not have an independent relational description, which prevents sound knowledge extraction from such networks. We present a methodology for generating independent propositional features for BCP by using semi-propositionalization of bottom clauses. Empirical results obtained in comparison with the original version of BCP show that this approach has comparable accuracy and runtimes, while allowing proper relational knowledge representation of features for knowledge extraction from CILP++ networks.