ULLER: A Unified Language for Learning and Reasoning
This work addresses the challenge of fragmentation in neuro-symbolic AI research, making it more accessible for newcomers and enabling better comparisons across frameworks, though it is incremental as it builds on existing systems.
The authors tackled the problem of heterogeneity in neuro-symbolic AI frameworks by proposing ULLER, a unified language that encompasses various settings and ensures compatibility with existing systems, aiming to improve accessibility and comparability in the field.
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.