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LLM pipeline translates institutional policies into machine-readable knowledge graphs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PolicyKG, an agentic LLM pipeline designed to convert institutional policies from natural language into machine-readable SHACL knowledge graphs. This system classifies policy sentences into obligations, permissions, or prohibitions, translates them into first-order deontic logic, and generates SHACL constraints. The pipeline utilizes a LangGraph state machine and a Corpus Adapter for domain retargeting, demonstrating high accuracy on institutional policy corpora and showing promise for compliance checking. AI

IMPACT Enables automated compliance checking and policy management by converting unstructured text into structured, machine-readable formats.

RANK_REASON The item is an academic paper detailing a novel LLM pipeline for a specific research task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM pipeline translates institutional policies into machine-readable knowledge graphs

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The item is an academic paper detailing a novel LLM pipeline for a specific research task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ponkrit Kaewsawee, Chaklam Silpasuwanchai, Chutiporn Anutariya ·

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

    arXiv:2608.09028v1 Announce Type: new Abstract: 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 polic…

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