A Domain-Specific Language for NeSy Focussing on Symbolic Knowledge Injection

In neuro-symbolic AI (NeSy), integrating symbolic languages – typically subsets of first-order logic (FOL) –, with neural networks (NNs) serves goals like enhancing symbolic processing, extending reasoning with pattern recognition, and guiding neural learning with symbolic knowledge—a.k.a. symbolic knowledge injection (SKI). Despite its utility, FOL’s expressiveness poses challenges to SKI algorithms, and its general-purpose nature complicates use for non-experts. We propose SKI-lang, a domain-specific language for SKI that balances practicality, clear semantics, and expressiveness–tractability trade-offs. SKI-lang simplifies symbolic specification, serves as a unified interface for diverse SKI approaches, and allows for automating benchmarks from NeSy literature. We discuss the design choices behind SKI-lang and its implementation, and demonstrate its effectiveness and versatility through a few case studies.