---
dataset_info:
  features:
  - name: wiki_title
    dtype: string
  - name: qid
    dtype: string
  - name: definition
    dtype: string
  splits:
  - name: en
    num_bytes: 26443378
    num_examples: 25449
  download_size: 16447664
  dataset_size: 26443378
configs:
- config_name: default
  data_files:
  - split: en
    path: data/en-*
license: mit
language:
- en
tags:
- Wikidata
- Wikipedia
- Description
- Entity
- QID
- Knowledge

---


# Wikipedia Definitions Dataset
`wikipedia_definitions` pairs English Wikipedia article titles (`wiki_title`) and their Wikidata IDs (`qid`) with the **definition sentence(s) that open each Wikipedia article**.  
The corpus contains **25 449 entities**.

Lead-paragraph definitions give a slightly richer, stylistically uniform overview of an entity than the short Wikidata description, making them useful as lightweight contextual signals for tasks such as entity linking, retrieval, question answering, knowledge-graph construction and many other NLP / IR applications.

---

## Dataset Structure
```python
from datasets import load_dataset
ds = load_dataset("masaki-sakata/wikipedia_definitions", split="en")
print(ds)
# Dataset({
#   features: ['wiki_title', 'qid', 'definition'],
#   num_rows: 25449
# })
```

Field description:

| column       | type | description                                                                    |
|--------------|------|--------------------------------------------------------------------------------|
| `wiki_title` | str  | Title of the corresponding English Wikipedia article                           |
| `qid`        | str  | Wikidata identifier, e.g. `Q7156`                                              |
| `definition` | str  | The first sentence(s) of the English Wikipedia lead paragraph (CC-BY-SA 3.0)   |

Example record
```json
{
  "wiki_title": "Michael Jordan",
  "qid": "Q41421",
  "definition": "Michael Jeffrey Jordan (born February 17, 1963), also known by his initials MJ, is an American businessman and former professional basketball player, who is ..."
}
```

---

## Quick Usage Example
```python
from datasets import load_dataset

ds = load_dataset("masaki-sakata/wikipedia_definitions", split="en")

# print the first three definitions
for record in ds.select(range(3)):
    print(record)
```

---

## Source & Construction

1. **Seed list**  
   The split “en” from [`masaki-sakata/entity_popularity`](https://huggingface.co/datasets/masaki-sakata/entity_popularity) supplies the `wiki_title` and `qid` pairs.

2. **Extraction**  
   For every `wiki_title` we queried the Wikipedia REST API (`page/summary`) and extracted the plain-text `extract` field, which corresponds to the first sentence(s) of the lead paragraph.

3. **Post-processing**  
   • Articles without a non-empty lead extract were discarded.  
   Resulting size: **25 449 items**.

4. **Licensing**  
   • Each definition is taken from English Wikipedia and is therefore licensed under CC-BY-SA 3.0 and GFDL.  
   • The dataset as a compilation (metadata, indexing and scripts) is released under the MIT License.  
   If you redistribute or use the text in downstream applications, remember to comply with CC-BY-SA 3.0 attribution and share-alike requirements.

---

Happy experimenting! Feel free to open an issue or pull request if you discover errors or have feature requests.