Knowledge graph

What is a knowledge graph?

A knowledge graph is a structured network of real-world entities, such as people, companies, places, products and concepts, linked by named relationships. Instead of storing text as strings of words, it stores facts: "BeBranded is a digital agency", "the agency offers SEO services". Search engines and AI systems use these connections to understand what a query is about, not only which words it contains.

Google's Knowledge Graph, launched in 2012 under the slogan "things, not strings", is the best-known example. It powers knowledge panels, entity cards and many direct answers on the results page.

How a knowledge graph works

A knowledge graph is built from three elements: entities (the nodes), attributes (facts about each node) and relationships (the links between nodes). Each fact is often expressed as a triple: subject, predicate, object.

• Subject: the entity described, for example a company.
• Predicate: the relationship, for example "founded by" or "located in".
• Object: the value or the linked entity, for example a person or a city.

Sources feed the graph: structured databases, Wikipedia and Wikidata, official websites and structured data published on pages. The system reconciles them, resolves duplicates and assigns each entity a unique identifier.

Why it matters for SEO and GEO

When a search engine recognises your brand as a distinct entity, it can show a knowledge panel, connect you with the right topics and trust the facts you publish. For generative engines, entity clarity helps answers cite the right company, product or author. This is a core idea behind generative engine optimization and E-E-A-T: being a known, consistent and verifiable entity.

The effect shows on the SERP through knowledge panels, rich results and AI Overviews that name sources.

Knowledge graph vs database vs schema markup

Schema markup is one input, the knowledge graph is the resulting understanding. You do not edit Google's graph directly: you send consistent signals and let the engine reconcile them.

How to feed a knowledge graph

• Publish consistent facts about your organisation (name, logo, address, founders, social profiles) on your website and external profiles.
• Add schema markup such as Organization, Person and Product, with sameAs links to trusted profiles.
• Create or update entries in reference sources like Wikidata when you meet their notability rules.
• Build an About page and author pages that state who you are and what you are expert in.
• Earn mentions on reliable third-party sites that describe you the same way.

Example of a minimal markup:

{"@type":"Organization","name":"BeBranded","url":"https://www.bebranded.xyz","sameAs":["https://www.linkedin.com/company/bebranded"]}

Inconsistent names or conflicting facts across sources are the main reason an entity is not recognised. Results are never guaranteed, since inclusion is decided by the engine.

Knowledge graph at BeBranded

At BeBranded, we help brands become clear, consistent entities: structured data, entity-focused content and a coherent presence across the web. Discover our work on the SEO and GEO service page.