{
  "id": 1844083,
  "title": "Neuro-symbolic learning over OWL 2 DL via consequence-based compilation to differentiable circuits",
  "url": "https://urgent.news/2026/08/18/neuro-symbolic-learning-over-owl-2-dl-via-consequence-based",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-18T13:04:51.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.17741v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}