# GRASP: Generic Reasoning And SPARQL Generation across Knowledge Graphs

arXiv: https://arxiv.org/abs/2507.08107 · HTML: https://mlsanity.com/paper/2507.08107/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 3 of its 52 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 3 of 3)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)

## Built on by (most cited first, 7 of 7)

- [SPARQL-LLM: Real-Time SPARQL Query Generation from Natural Language Questions](https://mlsanity.com/paper/2512.14277/) (2025)
- [Agentic SPARQL: Evaluating SPARQL-MCP-powered Intelligent Agents on the Federated KGQA Benchmark](https://mlsanity.com/paper/2603.06582/) (2026)
- [The Wikidata Query Logs Dataset](https://mlsanity.com/paper/2602.14594/) (2026)
- [GRISP: Guided Recurrent IRI Selection over SPARQL Skeletons](https://mlsanity.com/paper/2604.21133/) (2026)
- [MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQA](https://mlsanity.com/paper/2607.14561/) (2026)
- [Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation](https://mlsanity.com/paper/2607.18029/) (2026)
- [Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs](https://mlsanity.com/paper/2607.25959/) (2026)

## Abstract (arXiv)

We propose a new approach for generating SPARQL queries on RDF knowledge graphs from natural language questions or keyword queries, using a large language model. Our approach does not require fine-tuning. Instead, it uses the language model to explore the knowledge graph by strategically executing SPARQL queries and searching for relevant IRIs and literals. We evaluate our approach on a variety of benchmarks (for knowledge graphs of different kinds and sizes) and language models (of different scales and types, commercial as well as open-source) and compare it with existing approaches. On Wikidata we reach state-of-the-art results on multiple benchmarks, despite the zero-shot setting. On Freebase we come close to the best few-shot methods. On other, less commonly evaluated knowledge graphs and benchmarks our approach also performs well overall. We conduct several additional studies, like comparing different ways of searching the graphs, incorporating a feedback mechanism, or making use of few-shot examples.