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Wikidata · Knowledge Graph
Structured entity data feeds Google's Knowledge Graph and is queried directly by every major LLM. Wikidata cleanup raises the floor on how AI describes you — sometimes more than Wikipedia does.
What you get
Wikidata feeds Google's Knowledge Graph, ChatGPT, Gemini, Perplexity and other AI systems. We create or clean up your entity, add stable identifiers, and align multilingual labels.
Starting price
from €550 per entity
Typical timeline
1-2 weeks
Best for
Why structured data
Search engines and AI assistants no longer rank pages alone — they resolve entities. SEO describes you in prose; structured data defines you as a record. If no record exists, systems fall back on guessing.
Missing logo, outdated website, no social profiles. Google assembles much of that panel from structured sources like Wikidata — if the record is thin or wrong, the panel is too.
Assistants want instant, citable facts. They resolve entities against structured data and trusted corpora rather than parsing your marketing copy sentence by sentence.
In sensitive (YMYL) niches, a source-referenced entity in an open, human-edited knowledge base corroborates who you are in a way your own website cannot.
Wikipedia is written for humans. Wikidata is its machine-readable sibling: a database where every entity is an item (a Q-number) connected to values by properties (P-numbers). Each fact is a triple — subject, property, value — the native format of every knowledge graph. There is no prose to interpret and nothing lost in translation: one record carries labels in hundreds of languages.
This is the difference between being described and being defined. A blog post about your company is a string a crawler must interpret. A Wikidata item is a thing other systems can reference, query and build on — the distinction we unpack in AEO vs GEO vs SEO.
// things, not strings — how a machine reads Berlin
Berlin (Q64)
├─ instance of (P31) → city with millions of inhabitants
├─ country (P17) → Germany (Q183)
├─ population (P1082) → 3,677,472 (as of 31 Dec 2021)
└─ official website (P856) → berlin.de
Google can corroborate your logo, website and profiles against a referenced record. A panel becomes possible where it previously had nothing to verify.
One QID separates you from every same-named company, and multilingual labels tell systems that your brand in Berlin, Kyiv and New York is one entity.
When an assistant is asked who founded you or where you are based, it can ground the answer in the entity record instead of guessing from scattered pages.
Entity grounding is one layer of a broader program — see AI Visibility for how it combines with citations and coverage.
Know the difference
Three layers that get conflated constantly. They are different systems with different gatekeepers — and only two of them can be edited at all.
| Wikidata | Wikipedia | Google Knowledge Graph | |
|---|---|---|---|
| What it is | A machine-readable database of entities: Q-items connected by P-properties. | A human-readable encyclopedia of prose articles. | Google's internal entity database that powers search features. |
| Who writes it | Volunteer editors and bots; every statement should carry a reference. | Volunteer editors under strict notability and sourcing policy. | No one outside Google. Its systems assemble it from sources they trust. |
| What it feeds | Google's Knowledge Graph, LLM training and derived datasets, voice assistants. | AI answers and citations, journalists, due diligence — and the Knowledge Graph. | Knowledge Panels, AI Overviews, Google Assistant. |
| Entry bar | Lower: a clearly identifiable entity backed by serious, publicly available references. | High: sustained in-depth coverage in independent, reliable sources (GNG / NCORP). | None to apply to. Google admits entities it can verify across enough sources. |
| Our service | Entity creation & cleanup — €550 | Article creation — from €1,930 | Not directly editable — it is fed by the two columns to the left. We do not sell Knowledge Graph “placement”; no one honest can. |
Full price list across all services on the pricing page.
The entity record
A company item is a structured key-value record, not a sales page. Here is the typical property set we populate, and the markup that ties it to your domain.
Acme Robotics GmbH
Q-item · illustrativeIllustrative mockup — “Acme Robotics GmbH” is a fictional company. The property IDs are real Wikidata properties we typically populate for organizations.
The entity record alone is half the job. Your website's Organization markup should declare sameAs links to the Wikidata QID and your official profiles, while the Wikidata item points back to your domain via official website (P856). Two independent, machine-readable references to the same identity mean Google and LLM retrieval systems merge your signals into one entity — instead of guessing whether the LinkedIn page, the GitHub org and the website belong to the same company.
We deliver this ready-to-paste markup to your developer as part of every engagement. For the full mechanics of QIDs, entity reconciliation and panel triggers, read Wikidata and the Google Knowledge Graph.
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Acme Robotics GmbH",
"url": "https://acme-robotics.example",
"logo": "https://acme-robotics.example/logo.svg",
"sameAs": [
"https://www.wikidata.org/wiki/Q00000000",
"https://www.linkedin.com/company/acme-robotics",
"https://github.com/acme-robotics"
]
}From record to answer
Each hop is probabilistic, not contractual — but every hop starts from the same place: a referenced entity record.
Step 1
We publish or repair the entity: referenced claims, stable identifiers, multilingual labels and descriptions.
Step 2
Google reconciles the record with your site's schema.org markup and other trusted sources into one verified entity.
Step 3
Where Google judges the entity prominent enough, brand searches can render a panel with your logo, website and profiles.
Step 4
ChatGPT, Gemini and Perplexity ground brand facts in the same entity layer — directly and through derived datasets.
The record stays publicly editable after we ship it — that is why most clients add Wikimonitoring to catch unsourced or hostile edits early.
What we ship
A new Wikidata item with all the structured claims, identifiers and multilingual labels Wikipedia / Google / LLMs expect.
Audit and fix an existing item. Most public entities have outdated claims, missing identifiers, or wrong descriptions.
LEI, ISIN, ORCID, ROR, Crunchbase, MusicBrainz, IMDb — whatever applies. This is where AI retrieval looks.
Labels and descriptions across the languages your buyers and AI systems search in.
Note: we don't guarantee a Google Knowledge Panel — Google's systems make that call. Our work materially raises the probability.
Frequently asked questions
Where to next
Wikipedia AEO
The answer-engine layer Wikidata plugs into.
Wikidata & Google Knowledge Graph
How a Q-item becomes a Knowledge Panel.
Our machine-readable layer
We practice what we sell — see our own entity graph.
AI Visibility Packages
Wikidata work productised from €700.
SEO & LLMs Booster Pack
Wikidata entity bundled with 6 other high-authority platforms.
Share what you have — existing media, target languages, page URLs if any — and we'll come back with a realistic plan and price.