Neuro-symbolic approaches have garnered much interest recently as a path toward endowing neural systems with robust reasoning capabilities. Most proposed end-to-end methods assume knowledge to be given in advance and do not scale up over many latent concepts. The recently proposed Embed2Sym tackles the scalability limitation by
performing end-to-end neural training of a visual perception component from downstream labels to generate clusters in the latent space of symbolic concepts. These are later used to perform downstream symbolic reasoning but symbolic knowledge is still engineered. Taking inspiration from Embed2Sym, this paper introduces a novel method for scalable neuro-symbolic learning of first-order logic programs from raw data. The learned clusters are optimally labelled using sampled predictions of a pre-trained vision-language model. A SOTA symbolic learner, robust to
noise, uses these labels to learn an answer set program that solves the reasoning task. Our approach, called Embed2Rule, is shown to achieve better accuracy than SOTA neuro-symbolic systems on existing bench-mark tasks in most cases while scaling up to tasks that require far more
complex reasoning and a large number of latent concepts.