Relational Reasoning Networks

Understand

Neuro-symbolic methods integrate neural architectures, knowledge representation and reasoning.

• However, they have been struggling at both dealing with the intrinsic uncertainty of the observations and scaling to real-world applications.
• This paper presents Relational Reasoning Networks (R2N), a novel end-to-end model that performs relational reasoning in the latent space of a deep learner architecture, where the representations of constants, ground atoms and their manipulations are learned in an integrated fashion.
• Unlike flat architectures like Knowledge Graph Embedders, which can only represent relations between entities, R2Ns define an additional computational structure, accounting for higher-level relations among the ground atoms.