Recent Neural-Symbolic Approaches to ILP Based on Templates

Deep learning has been increasingly successful in the last few years, but its inherent limitations have recently become more evident, especially with respect to explainability and interpretability. Neural-symbolic approaches to inductive logic programming have been recently proposed to synergistically combine the advantages of inductive logic programming in terms of explainability and interpretability with the characteristic capability of deep learning to treat noisy, erroneous, and non-logical data. This paper surveys and briefly compares four relevant neural-symbolic approaches to inductive logic programming that have been proposed in the last five years and that use templates as an effective basis to learn logic programs from data.