Wikidata · Knowledge Graph

Wikidata is what AI reads when it reads about your brand.

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.

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What you get

Wikidata & Knowledge Graph

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.

Entity creation & cleanup
External identifier crosslinks (LEI, ISIN, ORCID, etc.)
Multilingual labels & descriptions
Feeds Knowledge Graph + major LLMs

Starting price

from €550 per entity

Typical timeline

1-2 weeks

Best for

· Brands missing from AI answers
· Multi-language entity hygiene
· Enterprises preparing for AI search

Why structured data

AI reads databases. Prove your existence.

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.

An empty or wrong Knowledge Panel

Missing logo, outdated website, no social profiles.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.

AI answers skip your About page

Assistants want instant, citable facts. They resolve entities against structured data and trusted corpora rather than parsing your marketing copy sentence by sentence.

Weak machine-readable trust signals

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.

The technology: things, not strings

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

What changes when the entity exists

Knowledge Panel eligibility

Google can corroborate your logo, website and profiles against a referenced record. A panel becomes possible where it previously had nothing to verify.

Disambiguation

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.

AI answer grounding

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

Wikidata vs Wikipedia vs Google Knowledge Graph

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

What we actually set — and how it links back to your site

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 · illustrative

instance of (P31): business enterprise
official website (P856): https://acme-robotics.example
logo image (P154): Acme-Robotics-logo.svg
industry (P452): industrial robotics
inception (P571): 2014
founded by (P112): Jane Doe · John Roe
headquarters location (P159): Munich, Germany
parent organization (P749): Acme Holding AG
employees (P1128): 120 (2025)
Legal Entity Identifier (P1278): 529900EXAMPLE0000000
OpenCorporates ID (P1320): de/HRB000000
LinkedIn company ID (P4264): acme-robotics
Crunchbase organization ID (P2088): acme-robotics
GitHub username (P2037): acme-robotics
X username (P2002): AcmeRobotics

Illustrative mockup — “Acme Robotics GmbH” is a fictional company. The property IDs are real Wikidata properties we typically populate for organizations.

Closing the loop with schema.org sameAs

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

How this flows into AI answers

Each hop is probabilistic, not contractual — but every hop starts from the same place: a referenced entity record.

Step 1

Wikidata

We publish or repair the entity: referenced claims, stable identifiers, multilingual labels and descriptions.

Step 2

Google Knowledge Graph

Google reconciles the record with your site's schema.org markup and other trusted sources into one verified entity.

Step 3

Knowledge Panel

Where Google judges the entity prominent enough, brand searches can render a panel with your logo, website and profiles.

Step 4

AI assistants

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

Four work-streams, one Wikidata entity

Entity creation

A new Wikidata item with all the structured claims, identifiers and multilingual labels Wikipedia / Google / LLMs expect.

Entity cleanup

Audit and fix an existing item. Most public entities have outdated claims, missing identifiers, or wrong descriptions.

Identifier crosslinks

LEI, ISIN, ORCID, ROR, Crunchbase, MusicBrainz, IMDb — whatever applies. This is where AI retrieval looks.

Multilingual labels

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

Answers to the questions that come up before signing

Wikidata specifics

Is Wikidata a backdoor into Wikipedia?

No. They are sister projects with separate rules and separate editor communities. A Wikidata item does not entitle you to a Wikipedia article and does not move you up any queue. What it does is establish a referenced, machine-readable identity — valuable on its own, and a sensible first step when Wikipedia's notability bar is still out of reach.

Can anyone edit our Wikidata entity?

Yes. Wikidata is an open knowledge base, so any editor can change a statement. That is exactly why we back every claim with references — referenced statements are far more likely to survive review — and why most clients pair entity work with monitoring, so unsourced or hostile edits are caught and corrected quickly.

How fast does a Knowledge Panel appear?

Where an entity is eligible, panels typically surface within 30-60 days of the Knowledge Graph ingesting consistent signals. There is no guarantee and no way to force it — Google's systems make the call. What we control is input quality: a referenced entity, stable identifiers, and schema.org markup that all point to the same identity.

Do LLMs really read Wikidata?

Yes, in two ways. Directly: Wikidata's structured exports and query service are standard ingredients in retrieval and knowledge-graph pipelines. Indirectly: its facts propagate into the derived datasets and training corpora large models learn from, and into Google's Knowledge Graph that powers AI Overviews. No single edit flips an AI answer overnight — the entity layer raises the floor on how systems describe you.

Guarantees & risk policy

Do you guarantee that a page will be published?

No — and we don't believe anyone should. Wikipedia is an independent community with its own editorial review. What we do guarantee is a risk-managed process: we don't recommend going to publication unless the source base supports notability, and we run a 93% success rate on the pages we do take on.

What if a page is deleted after publication?

Within the 90-day monitoring window, we defend the page and republish if necessary. If we fail to restore a deleted page after three attempts within that window, we refund 80% of the project fee.

Can you guarantee a Google Knowledge Panel?

No. Knowledge Panels are driven by Google's own systems, fed in part by Wikidata and Wikipedia. Our work materially raises the probability that a panel appears, but Google makes the final call.

Do you disclose that you're paid to edit Wikipedia?

Yes — on every project, every time. The Wikimedia Terms of Use require paid contributors to disclose the contributing firm, the client, and the intended beneficiary. We disclose on the editor's user page, on the article talk page at creation, and in edit summaries during the work. This is non-negotiable. We refuse projects that ask us to hide the relationship — undisclosed paid editing is the single most common reason this kind of work gets banned and pages get deleted.

AI visibility

Why does Wikipedia matter for ChatGPT and AI search?

Large language models and AI answer engines weight high-authority encyclopedic sources heavily. Brands that exist on Wikipedia and Wikidata are cited consistently and accurately; brands that don't are paraphrased or skipped.

How is AI visibility different from SEO?

SEO optimises a page you control. AI visibility builds the source base AI systems read about you — Wikipedia, Wikidata, Reddit threads, Quora answers, structured media. The work is editorial and source-first, not technical.

Can you guarantee your brand will appear in ChatGPT answers?

No — model providers control their own retrieval and training. We can build the source infrastructure that materially raises the probability, and we monitor how major AI systems describe your brand over time.

Can we get a Google Knowledge Panel without a Wikipedia page?

Sometimes. A well-structured Wikidata entity, paired with consistent independent coverage and clean schema markup on your owned properties, can be enough to trigger a panel for a sufficiently well-known subject. But Wikipedia remains by far the strongest single signal, and panels generated without it are thinner and more volatile. If a Wikipedia page isn't on the table yet, we'll start with Wikidata + source-base work and tell you honestly what coverage that gets you.

Where to next

Pages readers open after this one

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.

Ready to start? Let's run a source assessment first.

Share what you have — existing media, target languages, page URLs if any — and we'll come back with a realistic plan and price.

Request Assessment

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.

{ "@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" ] }