CashlessConsumer/wikipedia-dyk-dataset

Wikipedia DYK (Did You Know) Dataset

A comprehensive database of Wikipedia's "Did You Know" (DYK) entries - interesting facts from Wikipedia's newest content that appeared on the Main Page.

Quick Start

import duckdb

# Connect to the dataset
conn = duckdb.connect('wikipedia-dyk.duckdb')

# Get a random fact
result = conn.execute("""
    SELECT date, content, primary_article, domain 
    FROM dyk_entries 
    ORDER BY RANDOM() 
    LIMIT 1
""").fetchone()
print(f"{result[0]}: {result[1]}")

conn.close()

Dataset Structure

Tables

dyk_entries

Column	Type	Description
date	DATE	Date the fact appeared on Wikipedia's Main Page
raw_text	TEXT	Original wiki-formatted text of the DYK entry
content	TEXT	Clean plain text version of the fact
primary_article	VARCHAR	Main Wikipedia article the fact is about
article_links	JSON	Array of all linked articles with display text
categories	JSON	Inferred categories (e.g., ["Science", "Physics"])
domain	VARCHAR	Broad domain classification
has_image	BOOLEAN	Whether the entry had an associated image
word_count	INTEGER	Word count of the content
source_url	VARCHAR	URL of the source archive page

Statistics

• Total entries: 118,258 (deduplicated)
• Date range: 2004-03-01 to 2026-03-20
• Domains: 14 categories
• Quality: 100% field coverage, verified

Building the Database from Scratch

This section explains how to rebuild the entire dataset from the raw HTML source files.

Prerequisites

pip install duckdb requests beautifulsoup4

HTML Source Files

The html_sources/ directory contains 267 raw Wikipedia DYK archive pages:

• cache/*.html - Monthly archives from Wikipedia
• source/*.html - Historical archives and recent additions

Each HTML file contains DYK entries in the format:

<li>... that [interesting fact]?</li>

Build Pipeline

Run the 8-step pipeline:

# Step 1: Initialize database
python ingest/init_db.py

# Step 2: Ingest from HTML sources
python ingest/ingest.py --source html_sources/

# Step 3: Backfill primary_article from source files
python ingest/backfill_from_source.py

# Step 4: Backfill red links (articles that didn't exist when DYK was published)
python ingest/backfill_redlinks.py

# Step 5: Augment with category/domain metadata
python ingest/augment_categories.py --all

# Step 6: Fix image marker inconsistencies
python ingest/fix_image_markers.py

# Step 7: Remove duplicate entries
python ingest/deduplicate.py

# Step 8: Verify quality
python ingest/verify_dataset.py

Or use the master pipeline script:

python ingest/build_pipeline.py --all  # Full rebuild
python ingest/build_pipeline.py --verify-only  # Verify existing data

Pipeline Details

Step 1: Init DB

Creates dyk_entries table with indexes on date and primary_article.

Step 2: Ingest

Parses HTML files to extract:

• Entry text
• Date from section headers
• Wiki links from <a href="/wiki/..."> tags

Step 3: Backfill from Source

For each entry missing primary_article:

1. Match by date (year+month from filename)
2. Normalize content (remove special chars, spaces)
3. Fuzzy match to find corresponding source entry
4. Extract article links from matched source

Step 4: Backfill Red Links

For remaining unmatched entries, look for:

• Standard wiki links: /wiki/Article_Name
• URL-encoded links: /wiki/Article_Name_(film)

Step 5: Augment Categories

Keyword-based classification into 40+ categories:

• Primary domains: Geography, History, Science, Arts, Sports, etc.
• Sub-categories: extracted from article names and content

Step 6: Fix Image Markers

Ensures has_image boolean is consistent with (pictured) / (shown) in text.

Step 7: Deduplicate

Removes duplicate entries based on (date, content) pairs, keeping first occurrence.

Step 8: Verify

Checks for:

• Null values in required fields
• Invalid JSON in article_links
• Duplicate entries
• Image marker consistency

Publishing to HuggingFace

1. Create HuggingFace Dataset

from huggingface_hub import HfApi

api = HfApi()
api.create_repo(repo_id="your-username/wikipedia-dyk", repo_type="dataset", exist_ok=True)

2. Upload Data

from huggingface_hub import HfApi

api = HfApi()

# Upload the DuckDB file
api.upload_file(
    path_or_fileobj="wikipedia-dyk.duckdb",
    path_in_repo="wikipedia-dyk.duckdb",
    repo_id="your-org/wikipedia-dyk-dataset",
    repo_type="dataset",
)

# Upload HTML sources (in batches for large files)
api.upload_folder(
    folder_path="html_sources/",
    repo_id="your-org/wikipedia-dyk-dataset",
    repo_type="dataset",
    ignore_patterns=["*.git*"],
)

3. Create Dataset Card

Create README.md with:

• Dataset description
• Citation info
• License (CC BY-SA 4.0 for Wikipedia content)

License

Wikipedia DYK entries are sourced from Wikipedia and subject to CC BY-SA 4.0 license. Individual article links are owned by their respective creators.

Acknowledgments

Data sourced from Wikipedia:Did you know archive.