ChatGPT_Law_PropertyGraph: Automated Analysis of Regulatory Documents

Overview

ChatGPT_Law_PropertyGraph is a modular Java-based software pipeline that transforms legal PDF documents into structured knowledge, supporting compliance checking with the European data regulatory framework. The system produces:

• A property graph in GraphML format
• A Markdown checklist outlining obligations, rights, and prohibitions

This project was developed as part of a thesis and uses the EU AI Act as a primary use case.

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High-level architecture of the legal document analysis pipeline.

Project Structure

The repository consists of three independent Maven projects, each located in its own subdirectory:

/LegislativeTextParser  
/LawToPropertyGraphGenerator  
/GraphMLToChecklist

Components at a Glance

• LegislativeTextParser: Extracts and cleans legal text from PDF files, preserving legal structure (Law → Chapter → Article → Paragraph).
• LawToPropertyGraphGenerator: Uses a Large Language Model (GPT-4o-mini) to extract legal entities and relationships, producing a property graph.
• GraphMLToChecklist: Generates a Markdown checklist summarizing legal responsibilities.

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Pipeline Components

1. Legislative Text Parser (/LegislativeTextParser)

• Parses and cleans legal PDF documents.
• Extracts hierarchical structure: Law → Chapter → Article → Paragraph.
• Outputs:
  • law_structure.json
  • entities.txt to: src/resources/output/entities.txt

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2. Law-to-Property-Graph Generator (/LawToPropertyGraphGenerator)

• Uses OpenAI GPT-4o-mini to extract legal entities and their verbal relations.
• Applies Levenshtein distance for entity normalization.
• Constructs a unified property graph.

Required Setup

• Create a file named env.properties in the root of this project with the following content:

  GPT_API_URL=https://api.openai.com/v1/chat/completions
  GPT_API_KEY=your_openai_key_here
  

• Copy entities.txt from the first project's output:

  From: /LegislativeTextParser/src/resources/output/entities.txt  
  To:   /LawToPropertyGraphGenerator/src/resources/output/entities.txt
  

• Output:

  • final.graphML in src/resources/output/

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3. GraphML-to-Checklist (/GraphMLToChecklist)

• Loads the GraphML file into a JavaFX interface.
• Generates a Markdown checklist summarizing the graph nodes.

Usage Note

• On startup, load the following files:
  • GraphML:
    /LawToPropertyGraphGenerator/src/resources/output/final.graphML
  • JSON:
    Output from /LegislativeTextParser (e.g. law_structure.json)

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Example Workflow

# 1. Run the legislative parser
cd LegislativeTextParser
mvn clean install
java -jar target/legislative-text-parser.jar path/to/AI_Act.pdf

# 2. Copy the entities file to the second tool
cp src/resources/output/entities.txt ../LawToPropertyGraphGenerator/src/resources/output/

# 3. Add API credentials to env.properties in the second tool's root

# 4. Run the property graph generator
cd ../LawToPropertyGraphGenerator
mvn clean install
java -jar target/law-to-property-graph-generator.jar

# 5. Start the checklist generator and load the required files
cd ../GraphMLToChecklist
mvn clean install
java -jar target/graphml-to-checklist.jar

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Technologies Used

• Java 21
• Maven
• JavaFX
• OpenAI GPT-4o-mini API
• GraphML (yEd-compatible)
• JSON & Markdown

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Authors

Developed by Papadopoulos Konstantinos,
Undergraduate student, Department of Computer Science and Engineering,
University of Ioannina

Supervised by Professor Panos Vasileiadis