mlsanityData to 14 Oct 2026

LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs

arXiv 2410.06062 ↗First posted 8 Oct 2024cs.DB cs.AI cs.IRcited by 22

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

We introduce a Retrieval-Augmented Generation (RAG) system for translating user questions into accurate federated SPARQL queries over bioinformatics knowledge graphs (KGs) leveraging Large Language Models (LLMs). To enhance accuracy and reduce hallucinations in query generation, our system utilises metadata from the KGs, including query examples and schema information, and incorporates a validation step to correct generated queries. The system is available online at chat.expasy.org.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 5 of its 12 references that are in the map. First posted on arXiv 8 Oct 2024.

Builds on

5 of 12 references.

References · 5

  1. 2022MTEB: Massive Text Embedding Benchmarkcited by 640
  2. 2023DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graphcited by 18
  3. 2024Assessing SPARQL capabilities of Large Language Modelscited by 14
  4. 2023On the Potential of Artificial Intelligence Chatbots for Data Exploration of Federated Bioinformatics Knowledge Graphscited by 9
  5. 2024Generalist embedding models are better at short-context clinical semantic search than specialized embedding modelscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 10; 2026: 11.

22 papers.

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  1. 2024CypherBench: Towards Precise Retrieval over Full-scale Modern Knowledge Graphs in the LLM Eracited by 16
  2. 2025AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbotscited by 10
  3. 2025Enhancing Text2Cypher with Schema Filteringcited by 6
  4. 2025SPARQL Query Generation with LLMs: Measuring the Impact of Training Data Memorization and Knowledge Injectioncited by 6
  5. 2025Reducing Hallucinations in Language Model-based SPARQL Query Generation Using Post-Generation Memory Retrievalcited by 3
  6. 2025SPARQL-LLM: Real-Time SPARQL Query Generation from Natural Language Questionscited by 3
  7. 2026Agentic SPARQL: Evaluating SPARQL-MCP-powered Intelligent Agents on the Federated KGQA Benchmarkcited by 3
  8. 2025FRASE: Structured Representations for Generalizable SPARQL Query Generationcited by 2
  9. 2025Enhancing Frame Detection with Retrieval Augmented Generationcited by 1
  10. 2026Query Symbolically or Retrieve Semantically? A Dataset and Method for Semi-Structured Question Answeringcited by 1
  11. 2025Q2Forge: Minting Competency Questions and SPARQL Queries for Question-Answering Over Knowledge Graphscited by 0
  12. 2025Knowledge Conceptualization Impacts RAG Efficacycited by 0
10 more
  1. 2025MetaboT: An LLM-based Multi-Agent Frameworkfor Interactive Analysis of Mass SpectrometryMetabolomics Knowledge Graphscited by 0
  2. 2026Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligencecited by 0
  3. 2026The Initial Exploration Problem in Knowledge Graph Explorationcited by 0
  4. 2026Competency Questions as Executable Plans: a Controlled RAG Architecture for Cultural Heritage Storytellingcited by 0
  5. 2026DeSQ: Decomposition-based SPARQL Query Generationcited by 0
  6. 2026MARS: Multi-hop Adaptive Retrieval and SPARQL Generation for KGQAcited by 0
  7. 2026AutoSchema: Live Schema Grounding for Agentic Text-to-Sparql over Heterogeneous Knowledge Graphscited by 0
  8. 2026KGVoyager: Knowledge Graph Agnostic Question Answering via Agentic Navigationcited by 0
  9. 2026Enabling Knowledge Graph Understanding at Scale with the EXplore Your Graphs ENgine (EXYGEN)cited by 0
  10. 2026Natural Language Knowledge Graph Query Execution: Leveraging Controlled Semantics in the LLM Context Windowcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
  2. 2025Augmented Knowledge Graph Querying leveraging LLMs
  3. 2024From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems
  4. 2024T-RAG: Lessons from the LLM Trenches
  5. 2025Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph
  6. 2024Meta Knowledge for Retrieval Augmented Large Language Models
  7. 2024Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases
  8. 2024RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG