  Neuro-symbolic systems (NeSy), which claim to combine the best of both
learning and reasoning capabilities of artificial intelligence, are missing a
core property of reasoning systems: Declarativeness. The lack of
declarativeness is caused by the functional nature of neural predicates
inherited from neural networks. We propose and implement a general framework
for fully declarative neural predicates, which hence extends to fully
declarative NeSy frameworks. We first show that the declarative extension
preserves the learning and reasoning capabilities while being able to answer
arbitrary queries while only being trained on a single query type.
