A knowledge graph is a database that stores facts as a network of connected things, such as people, companies, places and products, along with the relationships between them. Instead of storing a page of text, it stores statements like "this company was founded by this person" or "this product is made by this brand."
When marketers say "the Knowledge Graph," they usually mean Google's, the system behind knowledge panels and many quick facts in search results. But the idea is broader. Wikidata, enterprise data platforms and AI systems all use knowledge graphs to understand how the world fits together.
What is a knowledge graph?
Think of a knowledge graph as a giant map of dots and lines. Each dot is an entity, a distinct thing that can be identified. Each line is a relationship, such as "located in," "CEO of" or "author of." Every dot also carries attributes, like a founding date, a logo or a website.
Google introduced its Knowledge Graph in May 2012. In the launch post, Google described the shift as moving to "things, not strings," and said the graph contained more than 500 million objects and over 3.5 billion facts and relationships at the time. Google's current help documentation describes it as holding billions of facts.
Google's graph is not the only one. Wikidata is a free, open knowledge base that humans and machines can both read and edit, and it holds more than 90 million items as of October 2026. Each item has a unique "Q" identifier, labels, descriptions and statements backed by references.
Why knowledge graphs matter for search and AI
Knowledge graphs help search engines understand meaning, not just matching words. Google's launch post laid out three benefits that still hold: telling apart things that share a name, showing a useful summary of key facts, and surfacing related topics people may want to explore next.
For businesses, the most visible result is the knowledge panel, the information box that can appear for brands, people and organizations. Google explains that panels are generated automatically when enough information exists on the open web, and that Google's systems decide when, where and how they appear.
Knowledge graphs also matter for generative engine optimization (GEO). AI assistants need reliable facts to avoid confusing one business with another. Search-connected AI features can draw on structured knowledge alongside web pages, so accurate, consistent facts about your brand give these systems a better starting point. Exactly how each AI product uses knowledge graphs is not fully disclosed, so it is wise to treat this as one input among many.
How a knowledge graph works
Nodes, edges and attributes
The basic building blocks are simple. Nodes are entities. Edges are the relationships between them. Attributes are facts that describe a single node. For example, a company node might connect to a person node through a "founder" edge, and carry attributes like headquarters and founding year.
Where the facts come from
According to Google, Knowledge Graph information comes from three main places: public sources across the web, data Google licenses (for things like sports scores, stock prices and weather), and information submitted directly by content owners, including suggested changes on claimed panels.
Shared vocabularies
Graphs need a common language for types and relationships. Google's Knowledge Graph Search API returns results using standard schema.org types like Organization, Person, LocalBusiness, Event and WebSite. That same vocabulary powers the schema markup site owners add to their pages, which is one way your website can describe itself in terms a graph understands.
Updates and corrections
Knowledge graphs change constantly. Google says it relies mainly on automated systems to maintain quality, uses public feedback to find problems, and may remove information that is demonstrably false or outdated when reputable sources support the correction. Reasonably disputed facts may stay as they are.
A knowledge graph example in practice
Picture a hypothetical software company, "Northwind Analytics," with a founder named Dana Reyes. In a knowledge graph, Northwind is a node of type Organization. Dana is a node of type Person. A "founder" edge connects them. Northwind has attributes like its website, logo, headquarters city and founding year. It may connect to a product node, an industry category and a parent company.
When someone searches "who founded Northwind Analytics," a search engine with this graph can answer directly. When someone asks an AI assistant to compare Northwind with competitors, a clear picture of what Northwind is and does helps the assistant avoid mixing it up with an unrelated "Northwind" elsewhere.
Now imagine the facts are messy. Northwind's website says it was founded in 2015, its LinkedIn says 2016, and an old directory lists a former address. Each conflict is a small reason for a system to be less confident. Cleaning these up won't guarantee a panel, but it removes obvious sources of doubt.
How to help knowledge graphs understand your business
- Make your own site the clearest source. Publish a detailed About page with your official name, what you do, where you are, when you started and who leads the company, in plain crawlable text.
- Add Organization markup. Google's Organization structured data guide recommends placing it on your homepage or About page. Include name, url, logo, contact details and sameAs links to your official profiles.
- Keep facts consistent everywhere. Match your name, address, phone number, founding date and leadership across your site, Google Business Profile, LinkedIn, directories and press materials.
- Use Google Business Profile if you are a local business. Google points businesses to Business Profile, rather than the knowledge panel claim process, to manage their information.
- Claim your knowledge panel if one exists. If a panel appears for you or your organization, Google lets the subject or an official representative verify and suggest changes.
- Earn independent references. Coverage in news outlets, trade publications and reputable directories gives graphs more corroborating sources. A brand mention on a trusted site can help even without a link.
- Contribute to Wikidata only if you are genuinely notable. Add neutral, referenced statements and follow community guidelines. Promotional edits are often reverted.
Common mistakes and misconceptions
- "I can buy or force a knowledge panel." You can't. Google states that its systems decide whether a panel appears, and panels can stop showing over time. Be wary of anyone guaranteeing one.
- Assuming schema markup alone gets you into the graph. Markup is a useful signal, but Google also draws on public sources and licensed data. Your own claims carry more weight when others confirm them.
- Letting old information linger. Former addresses, outdated executives and abandoned social profiles keep feeding conflicting facts into the ecosystem.
- Confusing the Knowledge Graph API with the Knowledge Graph. The public Search API is a read-only window for developers, and Google notes it is migrating to Cloud Enterprise Knowledge Graph. It is not a tool for editing what Google shows in Search.
- Writing promotional Wikipedia or Wikidata entries. These communities remove self-promotional content. A deleted entry does more harm than no entry.
Knowledge graph vs. knowledge panel
The knowledge graph is the underlying database of entities and relationships. A knowledge panel is one way Google displays some of that information to searchers, usually as a box beside or above results. Many entities exist in the graph without ever getting a panel, and the facts in the graph also feed other features, such as quick answers and related searches.
How Butter approaches knowledge graphs
At Butter, we see knowledge graph work as making a brand easy to understand and hard to confuse. Our GEO plan includes knowledge graph submissions and schema markup guidance, which means we help clients get their core facts into the places that matter and describe their organization accurately on their own site.
We start by auditing how a brand appears today: its About page, structured data, Business Profile, social profiles and any existing panel. We look for conflicting facts and missing references, then prioritize fixes. Monthly AI prompt testing shows whether assistants describe the brand correctly, and when they don't, those gaps often point back to inconsistent or thin entity data. We're upfront that Google decides whether a panel appears, so our focus is accuracy and consistency rather than promises. You can see how this fits into our plans on the pricing page.
Frequently asked questions
How do I get my business into Google's Knowledge Graph?
There is no direct sign-up form. Google builds its graph from public web sources, licensed data and information content owners submit. Publishing clear, consistent facts on your site, using Organization markup and earning independent coverage all make your business easier for Google to understand.
Can I edit my knowledge panel?
If a panel already appears, the subject or an official representative can claim it and suggest changes through Google's verification process. Local businesses should manage their details through Google Business Profile instead. Anyone can also send feedback using the panel's feedback option.
Is Wikidata part of Google's Knowledge Graph?
Wikidata is a separate, open knowledge base run by the Wikimedia community. Google says its graph draws on public sources, and open databases like Wikidata are widely used as reference points. Google does not publish a full list of the sources it uses.
Do AI assistants use knowledge graphs?
Many AI systems combine language models with structured knowledge and live web results, but most vendors do not fully disclose how. Keeping your facts accurate and consistent across trusted sources is the practical step, since it helps whichever data an assistant draws on.