Graph data query and visualization via large language models

Abstract

In the evolving landscape of Large Language Models (LLMs), many applications are being discovered daily with breakneck speed. One notable application is the integration of LLMs into databases, enhancing the querying and visualization processes of graph-based data, which this paper will explore. Traditional graph databases often require complex query languages and extensive domain exper- tise, posing a high barrier of skills required for non-specialists to fully utilize their benefits. Specifically, this study aims to use a user-friendly interface for natural language input, to integrate fine-tuned LLMs for coding to dynamically generate Cypher queries for seamless interaction with graph databases. This al- lows users to analyze and visualize interconnected data with minimal technical expertise. By bridging the gap between technical and non-technical stakeholders, the system simplifies decision-making processes in domains such as social net- works, fraud detection, and supply chain optimization. Furthermore, this work lays the foundation for future advancements in natural language interfaces for graph databases, addressing a critical need in today’s increasingly data-driven world.