Neuro-symbolic learning over OWL 2 DL via consequence-based compilation to differentiable circuits
OWL 2 DL ontologies, grounded in the description logic $\mathcal{SROIQ}$, express large knowledge bases in biomedicine and the Semantic Web. Neuro-symbolic (NeSy) learners over description logics either embed the ontology in a continuous space, abandoning classical entailment, or restrict to the Horn fragment $\mathcal{EL}^{++}$, which has a single canonical model. We present Baobab, which…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
