Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning

📰 ArXiv cs.AI

Learn how to represent knowledge graphs for LLM-based policy compliance reasoning to ensure safe and secure AI integration

advanced Published 1 May 2026
Action Steps
  1. Build a knowledge graph from AI policy documents using ontology schemas
  2. Retrieve policy-relevant information from the knowledge graph to answer questions
  3. Evaluate the effectiveness of the knowledge graph in supporting LLM-based policy compliance reasoning
  4. Configure the agentic framework to construct knowledge graphs from various policy documents
  5. Test the framework's ability to retrieve accurate policy-relevant information
Who Needs to Know This

AI engineers, data scientists, and compliance officers can benefit from this knowledge to develop and implement policy-compliant AI systems

Key Insight

💡 Knowledge graphs can be used to represent policy compliance information for LLM-based reasoning

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🤖 Ensure policy compliance in AI systems with knowledge graph representations! 📊

Full Article

Title: Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning

Abstract:
arXiv:2604.27713v1 Announce Type: new Abstract: The risks posed by AI features are increasing as they are rapidly integrated into software applications. In response, regulations and standards for safe and secure AI have been proposed. In this paper, we present an agentic framework that constructs knowledge graphs (KGs) from AI policy documents and retrieves policy-relevant information to answer questions. We build KGs from three AI risk-related polices under two ontology schemas, and then evalua
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