Entities, not search queries
Most keyword sources show you what people type. This one shows you what exists. The tool uses Wikipedia's OpenSearch to return article titles that match your seed, in the language you choose. The output is a list of entities and topics: people, places, concepts, products, events and organizations.
That makes it a research tool for understanding a subject's landscape. A seed like "coffee" can surface articles on varieties, brewing methods, growing regions and the history of the trade, giving you a vocabulary that pure query lists often miss.
Three ways to use Wikipedia topics
1. Plan a topic cluster
Pick a broad seed and treat the results as a map. The main subject becomes your pillar page; the most relevant entities become supporting articles that link back to it. Because Wikipedia already organizes knowledge this way, the structure tends to be logical.
2. Fill gaps in a content outline
Writing a guide to "Kyoto"? Temples, districts, festivals and local dishes listed as articles hint at sections readers may expect. Covering the key related entities makes a page more complete.
3. Check naming
Wikipedia titles use the accepted name of a concept. They are a fast way to confirm the spelling of a proper noun or the standard term before using it in a title or meta description.
Getting results: keep the seed short
OpenSearch matches against article titles, and titles are short. Seeds of one or two words work best: "coffee", "stoicism", "solar panel". Long phrases, and especially long Vietnamese phrases, often return nothing because no article carries that title. For full search queries, use Keyword Suggest or the Question Keyword Finder instead.
- Enter a short seed and pick the Wikipedia language, plus the country whose Google data you want.
- Scan the list and filter by volume or KD.
- Re-run promising entities as new seeds to go one level deeper.
- Export to Excel/CSV, add entities to a keyword plan, or send them to the Keyword Grouper.
What the metrics tell you here
Each title is looked up in Google search data for average monthly search volume, a 12- or 24-month trend, CPC low and high, competition (0–100) and KD (5–99). Wikipedia is an encyclopedia, not a search engine, and has no search volume of its own, so Google volume is the demand signal.
- High volume entities usually deserve their own page.
- Low or zero volume entities still matter for context; cover them as sections inside a bigger article.
- A rising trend can flag an entity in the news, worth a timely piece.
KD is our estimate based on ad competition, keyword length and search volume rather than backlinks. Entity names are often short and broad, so expect higher KD on the most general ones.
Access
Sign in free with Google or a Gmail link, no card needed. The free plan includes 10 credits a day and full data for the first 5 results. Lite costs $2/month (1,000 credits), Starter $9/month (10,000) and Pro $19/month (25,000); each search costs one credit.
Frequently asked questions
Are these results real search queries?
No. They are Wikipedia article titles that match your seed, so they represent topics and entities. The Google search volume next to each tells you which ones people search for.
Why does a long phrase return no results?
The tool matches article titles, which are short. Use one or two words as the seed. For long, question-style queries, Google or Yahoo suggestions work better.
Which Wikipedia languages are supported?
Results come from the Wikipedia edition for the language you select. Larger editions such as English usually return more titles than smaller ones.
How do entities help SEO?
They help you plan topic clusters, spot missing sections in an outline and use the standard names for concepts, which makes content more complete and easier to understand.