🌐 Wikidata Entity Search Scraper

🚀 Search Wikidata's open knowledge graph of 100M+ entities by name.

🕒 Last updated: 2026-05-06 · 📊 22 fields per record · 100M+ entities · people, places, brands, books, films, concepts · all claims, sitelinks, multilingual labels

The Wikidata Entity Search Scraper searches Wikidata's open knowledge graph of 100M+ entities by name. Output includes the canonical Q-ID, label, description, aliases, all claims (P-properties), sitelinks to every Wikipedia language edition, and structured facts.

Wikidata is the structured-data backbone of Wikipedia and one of the largest open knowledge graphs in the world. Filters run server-side, so a single run can resolve every entity matching a name, fetch full claim trees, or pull entities in non-English languages.

🎯 Target Audience	💡 Primary Use Cases
ML pipelines, knowledge-graph engineers, journalists, fact-checkers, content recommendation engines, search developers	Entity resolution, knowledge-graph augmentation, fact-checking, content enrichment, multilingual search, ML training datasets

---

📋 What the Wikidata Entity Search Scraper does

Five filtering workflows in a single run:

• 🔍 Free-text search. Match entity labels and aliases.
• 🌐 Multilingual. Search in 20+ languages (en, es, fr, de, it, ja, zh, ko, ar, hi, pt, nl, ru).
• 🆔 Item or property. Search Q-entities (items) or P-entities (properties).
• 📊 Full claims fetch. Optional: pull every statement, sitelink, and structured fact per entity.
• 🏷️ Image extraction. Auto-extracts the entity's primary image from claim P18.

💡 Why it matters: clean, server-side filtering removes the parser-and-pagination work from your team and keeps your dataset fresh on every run.

---

🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough showing how to go from sign-up to a downloaded dataset.

---

⚙️ Input

Input	Type	Default	Behavior
maxItems	integer	10	Records to return. Free plan caps at 10, paid plan up to 1,000,000.
query	string	"tesla"	Term to search Wikidata entities.
language	string	"en"	Search language code (ISO 639).
entityType	string	"item"	`item` (Q) or `property` (P).
fetchClaims	boolean	true	Fetch full claims, sitelinks, aliases per entity.

Example: all entities matching Tesla.

{
    "maxItems": 50,
    "query": "tesla",
    "language": "en",
    "fetchClaims": true
}

Example: Spanish-language Madrid entities.

{
    "maxItems": 100,
    "query": "madrid",
    "language": "es"
}

---

📊 Output

Each record contains 22 fields. Download the dataset as CSV, Excel, JSON, or XML.

🧾 Schema

Field	Type	Example
🖼️ thumbnailUrl	string	null
🆔 entityId	string	"Q478214"
📛 label	string	"Tesla"
📝 description	string	"American automotive, energy storage and solar power company"
🏷️ aliases	array	["Tesla Inc","Tesla Motors"]
🆔 instanceOfId	string	null
📊 sitelinkCount	number	106
📊 claimCount	number	147
📊 claims	object	{P31:[Q43229],P159:[Q485176]}
🌐 wikidataUrl	string	"https://www.wikidata.org/wiki/Q478214"
📚 wikipediaEnUrl	string	"https://en.wikipedia.org/wiki/Tesla,_Inc."

📦 Sample records

---

✨ Why choose this Actor

	Capability
📚	100M+ entities. People, places, brands, books, films, concepts in a single query.
🌐	Multilingual. 20+ languages with native-language labels and aliases.
📊	Full structured facts. All P-property claims for entity-resolution pipelines.
🔗	Sitelinks to Wikipedia. Direct links to every Wikipedia language edition.
⚡	Fast. 100 entities in under 30 seconds.

---

📈 How it compares to alternatives

Approach	Cost	Coverage	Refresh	Filters	Setup
⭐ This Actor	$5 free credit	100M+ entities	Live per run	query, language, type, claims	⚡ 2 min
Wikidata SPARQL endpoint	Free	All	Live	SPARQL	🐢 SPARQL knowledge
Manual Wikidata browse	Free	Manual	Live	Web filters	🕒 Manual
DBpedia	Free	Subset	Stale	SPARQL	🐢 Setup

Pick this Actor when you want broad coverage, server-side filtering, and no pipeline maintenance.

---

🚀 How to use

1. 📝 Sign up. Create a free account with $5 credit (takes 2 minutes).
2. 🌐 Open the Actor. Go to the Wikidata Entity Search Scraper page on the Apify Store.
3. 🎯 Set input. Pick your filters and maxItems.
4. 🚀 Run it. Click Start and let the Actor collect your data.
5. 📥 Download. Grab your results in the Dataset tab as CSV, Excel, JSON, or XML.

⏱️ Total time from signup to downloaded dataset: 3-5 minutes. No coding required.

---

💼 Business use cases

🤖 Knowledge Graphs Entity resolution and disambiguation Augment internal KGs with Wikidata facts Build cross-language entity links Train named-entity-recognition models	🔍 Search & Discovery Power semantic search with structured facts Build autocomplete with multilingual labels Resolve ambiguous entity names Cross-language search experiments
📰 Journalism & Fact-Checking Verify entities mentioned in stories Pull biographical and corporate facts Cross-reference claims via P-properties Map relationship networks	🤖 ML & NLP Train entity-linking models Build retrieval-augmented agents Generate training datasets for NER Multilingual KB embedding

---

🔌 Automating Wikidata Entity Search Scraper

Control the scraper programmatically for scheduled runs and pipeline integrations:

• 🟢 Node.js. Install the apify-client NPM package.
• 🐍 Python. Use the apify-client PyPI package.
• 📚 See the Apify API documentation for full details.

The Apify Schedules feature lets you trigger this Actor on any cron interval. Hourly, daily, or weekly refreshes keep downstream databases in sync automatically.

---

🌟 Beyond business use cases

Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.

🎓 Research and academia Knowledge-graph research Reproducible KB snapshots Cross-cultural KB studies Course material on Wikidata	🎨 Personal and creative Personal knowledge dashboards Side projects with structured facts Newsletter content Hobbyist KB exploration
🤝 Non-profit and civic Open-knowledge contributions Civic literacy datasets Cultural heritage cataloging Multilingual literacy projects	🧪 Experimentation Train entity-linking ML models Prototype KB-aware chat agents Build entity-resolution pipelines Test cross-language search

---

🤖 Ask an AI assistant about this scraper

Open a ready-to-send prompt about this ParseForge actor in the AI of your choice:

• 💬 ChatGPT
• 🧠 Claude
• 🔍 Perplexity
• 🅒 Copilot

---

❓ Frequently Asked Questions

🧩 How does it work?

Provide a query and language. The Actor queries Wikidata's wbsearchentities endpoint and optionally fetches full claims via wbgetentities.

📊 How many fields per record?

22 base fields plus a claims object with every P-property and a sitelinks map across Wikipedia languages.

🌐 Which languages are supported?

20+, including English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Japanese, Chinese, Korean, Arabic, Hindi, Turkish, Polish, Swedish, Finnish, Danish, Norwegian, Czech.

🆔 What's the difference between Q and P entities?

Q-entities are items (people, places, things). P-entities are properties (relations like 'instance of', 'located in', 'date of birth').

🔁 Can I schedule runs?

Yes. Use Apify Schedules to refresh entity caches or track entity creations on a topic.

⚖️ Is this data public?

Yes. Wikidata publishes under CC0; you can use the data freely without attribution.

💳 Do I need a paid Apify plan?

No. The free plan covers preview runs. A paid plan unlocks higher item counts and scheduling.

🆘 What if a run fails?

Apify retries transient errors. Partial datasets are preserved.

🖼️ Does it return entity images?

Yes when the entity has claim P18 set. The Actor extracts the Commons image URL automatically.

📚 How do I use the claims field?

Each P-property maps to an array of values. Decode P-IDs via Wikidata's property page.

---

🔌 Integrate with any app

Wikidata Entity Search Scraper connects to any cloud service via Apify integrations:

• Make - Automate multi-step workflows
• Zapier - Connect with 5,000+ apps
• Slack - Get run notifications in your channels
• Airbyte - Pipe data into your warehouse
• GitHub - Trigger runs from commits and releases
• Google Drive - Export datasets straight to Sheets

You can also use webhooks to trigger downstream actions when a run finishes.

---

🔗 Recommended Actors

• 📚 Open Library Books - 30M+ books and editions
• 📖 Project Gutenberg Books - 75,000+ free public-domain books
• 🎨 Openverse Media - 800M+ openly licensed images and audio
• 📰 Hacker News Search - Every HN story since 2007
• 🌏 World Bank Indicators - Country economic indicators

💡 Pro Tip: browse the complete ParseForge collection for more reference-data scrapers.

---

🆘 Need Help? Open our contact form to request a new scraper, propose a custom data project, or report an issue.

---

⚠️ Disclaimer: this Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by the Wikimedia Foundation, Wikidata, Wikipedia, or any contributing editor. All trademarks mentioned are the property of their respective owners. Only publicly available open data is collected.