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PolicyKG: An Agentic LLM Pipeline for Translating Institutional Policies into SHACL Knowledge Graphs — arXiv:2608.09028

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.09028
License (as archived): CC0-1.0
Archived: 2026-10-19T08:50:54.666Z
SHA-256 of the archived text: d6d5e5f57209cec8d1fac1e52a656a0f6ab793bbcc2763b807a18520aaa78ccd
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How to cite

PolicyKG: An Agentic LLM Pipeline for Translating Institutional Policies into SHACL Knowledge Graphs — arXiv:2608.09028 — arXiv (en), https://arxiv.org/abs/2608.09028 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4987ccdedb83 (Metraipolis archive dc_4987ccdedb83, sha256 d6d5e5f57209cec8d1fac1e52a656a0f6ab793bbcc2763b807a18520aaa78ccd)

BibTeX

@misc{dc4987ccdedb83,
  title = {PolicyKG: An Agentic LLM Pipeline for Translating Institutional Policies into SHACL Knowledge Graphs — arXiv:2608.09028},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.09028},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4987ccdedb83; sha256 d6d5e5f57209cec8d1fac1e52a656a0f6ab793bbcc2763b807a18520aaa78ccd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.09028. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Institutional policies stay in natural language while the systems that check compliance demand machine-readable constraints. Bridging that gap is still done by hand. PolicyKG closes the loop. It is an LLM pipeline that reads a policy PDF, classifies each sentence as an obligation, permission, or prohibition, lifts the label into first-order deontic logic, and emits SHACL constraints. Four stages run on a LangGraph state machine with per-stage validators. The piece that matters most is the Corpus Adapter: a YAML vocabulary registry that grounds LLM predicates in a target ontology. Retargeting to a new domain means swapping the registry, not retraining a model. On the Asian Institute of Technology Policies and Procedures corpus (1,663 sentences, 443 rules), PolicyKG reaches 86.9% deontic classification accuracy (Cohen's kappa = .709). Three annotators independently re-label a 50-item sample and agree at Fleiss' kappa = .844. SHACL shape correctness on a 69-shape subset is F1 = .866. The FOL path handles 79.2% of rules; the rest go through a direct NL-to-SHACL fallback. We audited every one of the 443 rules for second- or higher-order constructs. An automated regex checklist flagged none, and a first-author pass on the 92 FOL-fallback cases confirmed the same. The exact upper 95% Clopper-Pearson bound on the true HOL rate is 0.67%. This is an audit finding for one corpus, not a proof of FOL sufficiency for institutional policy. Swapping the AIT registry for a GDPR registry raises exact property alignment from 1/15 to 11/15 (Fisher's exact p < .001; Cohen's h = 1.53). On the LexDeMod lease-contract benchmark (N = 200), Macro F1 drops to .370 because lease English uses "shall be entitled" for permission -- exactly the vocabulary mismatch registry swap is meant to fix. Repeated runs produce hash-identical SHACL outputs.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-11 in cs.CL).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Databases (cs.DB); Logic in Computer Science (cs.LO).
Pagina del lavoro: https://arxiv.org/abs/2608.09028 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T08:49:41.999Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).