Update README.md

## 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 part of a thesis on multilingual pretraining and cross lingual transferability. Its purpose is to facilitate research in text-to-SPARQL generation, particularly focusing on multilingual finetuning.

### Key Features:
* **Multilingual:** Covers 12 languages: English, German, Hebrew, Kannada, Chinese, Spanish, Italian, French, Dutch, Romanian, Farsi , and Russian.
* **Diverse Knowledge Graphs:** Includes queries for 15 KGs, prominently Wikidata and DBpedia.
* **Augmented Data:** Features German translations for many English questions and Wikidata entity/relationship mappings in the `context` column for most of the English and German queries.

## 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): (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 (as per thesis augmentation, the screenshot shows 895k)
* `test`: 788 rows (QALD-10 test set)

## 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

README.md

@@ -1,31 +1,50 @@
----
-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-*
----
+---
+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
+---