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---
license: mit
dataset_info:
features:
- name: text_query
dtype: string
- name: language
dtype: string
- name: sparql_query
dtype: string
- name: knowledge_graphs
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 374237004
num_examples: 895166
- name: test
num_bytes: 230499
num_examples: 788
download_size: 97377947
dataset_size: 374467503
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- text-generation
language:
- en
- de
- he
- kn
- zh
- es
- it
- fr
- nl
- ro
- fa
- ru
tags:
- code
size_categories:
- 100K<n<1M
---
## Dataset Description
This dataset contains **895,954 examples** of natural language questions paired with their corresponding SPARQL queries. It spans **12 languages** and targets **15 distinct knowledge graphs**, with a significant portion focused on Wikidata and DBpedia.
The dataset was developed as a contribution for the Master Thesis: *"Impact of Continual Multilingual Pre-training on Cross-Lingual Transferability for Source Languages"*. Its purpose is to facilitate research in text-to-SPARQL generation, particularly regarding multilinguality.
### Key Features:
* **Multilingual:** Covers 12 languages: English (en), German (de), Hebrew (he), Kannada (kn), Chinese (zh), Spanish (es), Italian (it), French (fr), Dutch (nl), Romanian (ro), Farsi (fa), and Russian (ru).
* **Diverse Knowledge Graphs**: Includes queries for 15 KGs, prominently Wikidata and DBpedia.
* **Large Scale:** Nearly 900,000 question-SPARQL pairs.
* **Augmented Data:** Features German translations for many English questions and Wikidata entity/relationship mappings in the `context` column for most of the examples of Wikidata in German and English.
## Dataset Structure
The dataset is provided in Parquet format and consists of the following columns:
* `text_query` (string): The natural language question.
* *(Example: "What is the boiling point of water?")*
* `language` (string): The language code of the `text_query` (e.g., 'de', 'en', 'es').
* `sparql_query` (string): The corresponding SPARQL query.
* *(Example: `PREFIX dbo: <http://dbpedia.org/ontology/> ... SELECT DISTINCT ?uri WHERE { ... }`)*
* `knowledge_graphs` (string): The knowledge graph targeted by the `sparql_query` (e.g., 'DBpedia', 'Wikidata').
* `context` (string, often null): (Optional) Wikidata entity/relationship mappings in JSON string format (e.g., `{"entities": {"United States Army": "Q9212"}, "relationships": {"spouse": "P26"}}`).
### Data Splits
* `train`: 895,954 rows.
* `test`: 788 rows.
## How to Use
You can load the dataset using the Hugging Face `datasets` library:
```python
from datasets import load_dataset
# Load a specific split (e.g., train)
dataset = load_dataset("julioc-p/Question-Sparql", split="train")
# Iterate through the dataset
for example in dataset:
print(f"Question ({example['language']}): {example['text_query']}")
print(f"Knowledge Graph: {example['knowledge_graphs']}")
print(f"SPARQL Query: {example['sparql_query']}")
if example['context']:
print(f"Context: {example['context']}")
print("-" * 20)
break |