aksw/Text2SPARQL-Clean

Dataset Card for Text2Sparql-Clean

🧾 Dataset Summary

Text2Sparql-Clean is a clean, standardized, and multilingual dataset for the task of translating natural language questions into SPARQL queries over the DBpedia knowledge graph. It aggregates and harmonizes four widely-used benchmarks in the text-to-SPARQL research field:

• —QALD (versions 1–9)
• —LC-QuAD 1.0
• —Orange/paraqa-sparqltotext
• —julioc-p/Question-Sparql

The dataset includes questions in English and Spanish, making it a valuable resource for both monolingual and cross-lingual question answering over knowledge graphs.

This is the second official version of the dataset.

🛠️ Key Features and Improvements

This dataset goes beyond simple aggregation. It includes several important improvements over the original datasets:

• —✅ Standardized SPARQL queries: All queries were revised to include the necessary PREFIX declarations, ensuring compatibility with DBpedia’s SPARQL endpoint.
• —✅ Syntactic and semantic validation: Every SPARQL query was checked for syntactic correctness and semantic executability. Faulty or malformed queries were either fixed or removed.
• —✅ Clean, high-quality corpus: The result is a clean dataset where all queries are both executable and meaningful, enabling reliable model training and evaluation.

✅ Supported Tasks

• —Text-to-SPARQL generation → Given a natural language question, generate the corresponding SPARQL query over DBpedia.

🌍 Languages

• —English (en)
• —Spanish (es)

📁 Dataset Format

Each data instance contains:

• —question: The question in natural language (English or Spanish)
• —query: The corresponding SPARQL query
• —dataset-id: Original dataset source (e.g., QALD-X, LCQUAD, Orange, JULIOC)

🔧 Dataset Creation Process

This dataset is the result of a comprehensive curation pipeline:

1. 1.Aggregation of four public datasets into a unified structure.
2. 2.QALD 1-9: https://github.com/ag-sc/QALD
3. 3.LC-QuAD 1.0: https://github.com/AskNowQA/LC-QuAD
4. 4.Paraqa-SparqlToText: https://huggingface.co/datasets/Orange/paraqa-sparqltotext
5. 5.Question-Sparql: https://huggingface.co/datasets/julioc-p/Question-Sparql
6. 6.Normalization of query formats and syntax, resulting in all queries with PREFIX.
7. 7.Insertion of missing PREFIX declarations to ensure compliance with DBpedia standards.
8. 8.Execution validation: All queries were tested against a DBpedia SPARQL endpoint. Queries that failed were either corrected (if trivial) or removed.
9. 9.Language consistency checks for both English and Spanish questions.

💡 Use Cases

• —Training and evaluating text-to-SPARQL models
• —Developing multilingual semantic parsers
• —Enabling cross-lingual question answering over structured knowledge graphs
• —Fine-tuning large language models on knowledge-graph-to-text tasks

📚 Citation

If you use this dataset in your research, please cite:

@misc{text2sparql-clean,
  author = {Marcos Gôlo, Paulo do Carmo, Edgard Marx, Ricardo Marcacini},
  title = {text2sparql-clean: Natural Language Text to SPARQL for DBpedia Cleaned Dataset},
  year = {2025},
  howpublished = {\url{https://huggingface.co/datasets/aksw/Text2Sparql-Clean}},
}

🪪 Licensing

license: apache-2.0

Check each dataset’s original repository for full licensing terms.