  The field of neuro-symbolic artificial intelligence (NeSy), which combines
learning and reasoning, has recently experienced significant growth. There now
are a wide variety of NeSy frameworks, each with its own specific language for
expressing background knowledge and how to relate it to neural networks. This
heterogeneity hinders accessibility for newcomers and makes comparing different
NeSy frameworks challenging. We propose a unified language for NeSy, which we
call ULLER, a Unified Language for LEarning and Reasoning. ULLER encompasses a
wide variety of settings, while ensuring that knowledge described in it can be
used in existing NeSy systems. ULLER has a neuro-symbolic first-order syntax
for which we provide example semantics including classical, fuzzy, and
probabilistic logics. We believe ULLER is a first step towards making NeSy
research more accessible and comparable, paving the way for libraries that
streamline training and evaluation across a multitude of semantics, knowledge
bases, and NeSy systems.
