Next-Gen Indonesia IT Law Q&A: Knowledge Graphs & LLMs

"Enhancing Indonesia's IT law Q&A with RAG and LLM—leveraging knowledge graphs for accurate, context-aware, and intelligent legal insights with real-time responses."

Comment 9

Inspiration

Understanding Indonesia's IT laws can be complex due to the vast number of regulations, articles, and interconnections. By leveraging Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and Knowledge Graphs, we aim to provide a smarter, context-aware Q&A system that enables legal professionals, researchers, and policymakers to retrieve precise legal insights efficiently.

What it does

This system allows users to:

  • Perform full-text searches across 63 Indonesia IT law regulations.
  • Identify the most influential regulations and explore their relationships.
  • Analyze connections between regulations and articles using graph-based analytics.
  • Visualize legal structures with matplotlib for better understanding.

How we built it

  1. Knowledge Graph Construction:
    • Built using ArangoDB with 63 regulations, 2,423 articles, and over 5,500 relationships including amendments, references, and hierarchical structures.
  2. Graph Analytics:
    • Integrated NetworkX to analyze shortest paths, centrality, and community structures.
  3. Natural Language Processing:
    • Utilized RAG with LLMs to provide context-aware responses based on graph retrieval.
  4. User Interface:
    • Developed with Gradio, running locally at http://127.0.0.1:7861.

Challenges we ran into

  • Efficiently indexing and querying large-scale legal documents while maintaining response speed.
  • Integrating graph-based retrieval with LLM-generated responses.
  • Designing an intuitive legal Q&A interface for non-technical users.

Accomplishments that we're proud of

  • Successfully integrated ArangoDB, NetworkX, and LLMs for graph-enhanced legal Q&A.
  • Enabled advanced legal reasoning by mapping the impact and references between laws.
  • Built an interactive visualization that helps users see legal connections dynamically.

What we learned

  • The power of RAG and LLMs in legal document retrieval.
  • How knowledge graphs enhance Q&A accuracy by structuring unstructured data.
  • The importance of graph analytics in legal research, especially in identifying key regulatory influences.

What's next for Next-Gen Indonesia IT Law Q&A: Knowledge Graphs & LLMs

  • Expanding the dataset to include more legal domains beyond IT law.
  • Enhancing real-time graph analytics for deeper legal insights.
  • Deploying the system as a cloud-based API for broader accessibility.
  • Improving natural language understanding to support multi-turn legal conversations.

Built With

+ 49 more
Share this project:

Updates