Interacting with knowledge graphs can be a daunting task for people without a background in computer science since the query language that is used (SPARQL) has a high barrier of entry. Large language models (LLMs) can lower that barrier by providing support in the form of Text2SPARQL translation. In this paper we introduce a generalized method based on SPINACH, an LLM backed agent that translates natural language questions to SPARQL queries not in a single shot, but as an iterative process of exploration and execution. We describe the overall architecture and reasoning behind our design decisions, and also conduct a thorough analysis of the agent behavior to gain insights into future areas for targeted improvements. This work was motivated by the Text2SPARQL challenge, a challenge that was held to facilitate improvements in the Text2SPARQL domain.
@article{arxiv.2510.02200,
title = {ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities},
author = {Felix Brei and Lorenz Bühmann and Johannes Frey and Daniel Gerber and Lars-Peter Meyer and Claus Stadler and Kirill Bulert},
journal= {arXiv preprint arXiv:2510.02200},
year = {2025}
}
Comments
peer reviewed publication at Text2SPARQL Workshop @ ESWC 2025