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Neuro-Symbolic Hierarchical Rule Induction
[article]
2021
arXiv
pre-print
We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in a hierarchical structure, first-order rules are invented by learning embeddings to match facts and body predicates of a meta-rule. To instantiate it, we specifically design an expressive set of generic meta-rules, and demonstrate they generate a consequent fragment of Horn clauses. During training, we inject a
arXiv:2112.13418v1
fatcat:pebig25vyvg5bl7rmfq2bbnipm