A hybrid view treats vectors and symbolic graphs as complementary rather than competing representations. Embeddings provide soft, similarity-based generalisation, while graph structure supplies explicit entities, predicates, and multi-hop paths. Neuro-symbolic rule learning follows this direction by combining continuous models with first-order logic. Yet the combination does not remove uncertainty: errors in entity resolution, missing edges, ontology design, and rule selection can propagate through the entire system.