GalaxyWeaver: Autonomous Table-to-Graph Conversion and Schema Optimization with Large Language Models

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Abstract

Most enterprise graph data derives from relational databases, yet transforming relational tables into query-optimized graph schemas remains challenging. Existing approaches have notable limitations: (1) transformations based on primary and foreign keys often fail to generate schemas optimized for query performance; (2) manual schema design, although flexible, is costly and requires domain expertise; and (3) machine learning methods predict graph structures based on data patterns but heavily depend on large, high-quality training datasets. To address these challenges, we propose GalaxyWeaver, a framework to automate query-aware graph schema generation. GalaxyWeaver utilizes the reasoning power of Large Language Models (LLMs) to align graph schema designs with specific query requirements, e#ectively integrating domain knowledge with optimization strategies. The framework employs prompt-guided analysis to enhance the decision-making accuracy of LLM agents, facilitating iterative schema refinement. Experiments across diverse domains show that GalaxyWeaver simplifies transformation while improving query performance and reducing storage costs.