# InteracSPARQL: An Interactive System for SPARQL Query Refinement Using Natural Language Explanations

arXiv: https://arxiv.org/abs/2511.02002 · HTML: https://mlsanity.com/paper/2511.02002/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 65 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 17 of 17)

- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [StructGPT: A General Framework for Large Language Model to Reason over Structured Data](https://mlsanity.com/paper/2305.09645/) (2023)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [SmBoP: Semi-autoregressive Bottom-up Semantic Parsing](https://mlsanity.com/paper/2010.12412/) (2020)
- [DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2210.00063/) (2022)
- [SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-DomainText-to-SQL Task](https://mlsanity.com/paper/1810.05237/) (2018)
- [SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions](https://mlsanity.com/paper/2407.11417/) (2024)
- [SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph](https://mlsanity.com/paper/2402.04627/) (2024)
- [A Universal Question-Answering Platform for Knowledge Graphs](https://mlsanity.com/paper/2303.00595/) (2023)
- [An Analytical Study of Large SPARQL Query Logs](https://mlsanity.com/paper/1708.00363/) (2017)
- [Knowledge Graph Question Answering Leaderboard: A Community Resource to Prevent a Replication Crisis](https://mlsanity.com/paper/2201.08174/) (2022)
- [A Survey of RDF Stores & SPARQL Engines for Querying Knowledge Graphs](https://mlsanity.com/paper/2102.13027/) (2021)
- [A Comprehensive Evaluation of Neural SPARQL Query Generation from Natural Language Questions](https://mlsanity.com/paper/2304.07772/) (2023)
- [Question Answering over Knowledge Graphs via Structural Query Patterns](https://mlsanity.com/paper/1910.09760/) (2019)
- [SPARQL Generation with Entity Pre-trained GPT for KG Question Answering](https://mlsanity.com/paper/2402.00969/) (2024)
- [SPARKLE: Enhancing SPARQL Generation with Direct KG Integration in Decoding](https://mlsanity.com/paper/2407.01626/) (2024)
- [The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing](https://mlsanity.com/paper/2305.15108/) (2023)

## Built on by (most cited first, 2 of 2)

- [LLM+Graph@VLDB'2025 Workshop Summary](https://mlsanity.com/paper/2604.02861/) (2026)
- [KGVoyager: Knowledge Graph Agnostic Question Answering via Agentic Navigation](https://mlsanity.com/paper/2609.01780/) (2026)

## Abstract (arXiv)

In recent years, querying semantic web data using SPARQL has remained challenging, especially for non-expert users, due to the language's complex syntax and the prerequisite of understanding intricate data structures. To address these challenges, we propose InteracSPARQL, an interactive SPARQL query generation and refinement system that leverages natural language explanations (NLEs) to enhance user comprehension and facilitate iterative query refinement. InteracSPARQL integrates LLMs with a rule-based approach to first produce structured explanations directly from SPARQL abstract syntax trees (ASTs), followed by LLM-based linguistic refinements. Users can interactively refine queries through direct feedback or LLM-driven self-refinement, enabling the correction of ambiguous or incorrect query components in real time. We evaluate InteracSPARQL on standard benchmarks, demonstrating significant improvements in query accuracy, explanation clarity, and overall user satisfaction compared to baseline approaches. Our experiments further highlight the effectiveness of combining rule-based methods with LLM-driven refinements to create more accessible and robust SPARQL interfaces.