ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912623566520320 |
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| author | Brei, Felix Bühmann, Lorenz Frey, Johannes Gerber, Daniel Meyer, Lars-Peter Stadler, Claus Bulert, Kirill |
| author_facet | Brei, Felix Bühmann, Lorenz Frey, Johannes Gerber, Daniel Meyer, Lars-Peter Stadler, Claus Bulert, Kirill |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02200 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities Brei, Felix Bühmann, Lorenz Frey, Johannes Gerber, Daniel Meyer, Lars-Peter Stadler, Claus Bulert, Kirill Computation and Language Artificial Intelligence 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. |
| title | ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.02200 |