What is AI Mode? Meaning, how it works and its impact on search
A knowledge graph represents knowledge as a network of entities and their relationships. Rather than storing information in isolation, it reveals how companies, people, products, services or topics are connected.
A marketing example: DREIKON → offers → SEO consultancy → improves → organic visibility. If you add relationships such as DREIKON → specialises in → GEO → improves → AI visibility, individual pieces of information come together to form an interconnected knowledge model.
A Knowledge Graph does more than simply store information; it makes the relationships between pieces of information explicit.
Key facts about the Knowledge Graph at a glance
- A knowledge graph links entities, properties and relationships.
- It is not the same as a graph database or the Google Knowledge Panel.
- Google uses its own Knowledge Graph to categorise real-world entities and their connections.
- Unique entities and semantic relationships are relevant for SEO and GEO.
- Knowledge graphs can also support AI, RAG and GraphRAG applications by providing structured context.
What is a knowledge graph?
A knowledge graph is a model for the structured representation of knowledge. Individual objects are recorded as entities or nodes and linked to one another via defined relationships.
In marketing, for example, DREIKON could be an entity. This is linked to SEO consultancy via the relationship ‘offers’. SEO consultancy, in turn, is linked to organic visibility via the relationship ‘improves’.
So-called ‘triples’ are typical:
DREIKON → offers → SEO consultancy
Content → addresses → search intent
GEO → enhances → SEO
Many such statements together form a network. The added value lies not in more data, but in more context.
What role do ontologies play?
In the case of more complex knowledge graphs, an ontology describes which types of entities and relationships are permitted. For example, it can specify that an agency offers services and that a service pursues a specific marketing objective. This creates a structured set of rules that links data semantically.
Knowledge graph, graph database and vector database – what is the difference?
Term | Main Task | Typical strength |
|---|---|---|
Knowledge Graph | Semantic Modeling of Knowledge and Relationships | Context and Meaning |
Graph Database | Storing and Retrieving Highly Interconnected Data | Efficient Traversal of Relationships |
Relational Database | Managing Structured Data in Tables | clear, stable data schemas |
Vector Database | Finding Similarities Between Vectors | Semantic Similarity and Retrieval |
A graph database can form the technical basis of a knowledge graph. However, it is not the same as the knowledge graph itself.
Vector databases also serve a different purpose: they identify semantically similar information. A knowledge graph, on the other hand, explicitly maps how entities are connected to one another. In modern AI systems, both approaches can be combined.
What a Knowledge Graph is not
Not purely a graph database: one is a knowledge model, the other a database technology.
Not a Knowledge Panel: The panel is a visible display of information on Google.
Not just Google: The Google Knowledge Graph is merely a specific application of the general concept.
Not a simple collection of data: it is the relationships between clearly defined entities that generate the actual added value.
What is the Google Knowledge Graph?
Google uses the Knowledge Graph to better categorise real-world entities and their relationships. As a result, a search query is not viewed solely as a sequence of keywords. For example, Google may attempt to recognise whether a name refers to a company, a person, a product or a place.
Google does not merely search for strings of characters. Google attempts to understand entities and their relationships.
This should be distinguished from the Knowledge Panel. This refers to a visible information box within Google Search. The Knowledge Graph is a knowledge structure, whilst the Knowledge Panel is one possible way of presenting the information derived from it.
Why is the Knowledge Graph relevant for SEO and GEO?
SEO is not moving away from keywords. SEO is expanding to include entities, relationships and context.
For search engines, it is not merely relevant that a website uses the term ‘SEO consultancy’, for example. They must be able to identify as clearly as possible which company is behind it, what services it offers and which topics it is associated with.
Structured data, consistent company information and entity consistency, amongst other factors, play a role in this.
This logic is also particularly relevant for GEO. GEO – Generative Engine Optimisation – extends SEO to include visibility in AI-generated responses. In order for AI systems to correctly categorise brands, services and expertise, they require clear and trustworthy signals.
However, a knowledge graph does not guarantee visibility in ChatGPT, Gemini or other AI systems. GEO is not a prompt hack. Authority, trustworthy sources, mentions and consistent entities remain crucial.
Knowledge Graph and AI: What do RAG and GraphRAG have to do with it?
In Retrieval-Augmented Generation (RAG), relevant information is first retrieved from a knowledge source and then provided to a language model as additional context.
Knowledge graphs can supplement this knowledge base. They do not merely find similar documents, but map out explicit relationships – for example:
GEO → enhances → SEO
GEO → drives → AI visibility
AI visibility → measurable via → brand mention rate
With GraphRAG, such graph-based relationships are specifically incorporated into the retrieval process.
Vector search identifies similarities. A knowledge graph provides relationships. Particularly when dealing with complex corporate knowledge, both approaches can be useful when used together.
What are some typical applications for knowledge graphs?
Concepts and entities are considered in context.
Knowledge spread across several systems is linked together.
Relationships between products, users or interests are taken into account in recommendations.
Topics, target audiences, services and search intent can be linked semantically.
Knowledge graphs can provide RAG and GraphRAG systems with structured context.
Relationships between accounts, individuals, transactions, devices or organisations can reveal suspicious patterns.
When is it worth having your own knowledge graph?
A knowledge graph is particularly useful when information is spread across many systems and the relationships between these pieces of information generate business value.
A marketing team could, for example, link keywords, content, services, target audiences and search intent. This makes it clear which content addresses which search intent, which service it relates to and which target audience it is relevant for.
For simple, clearly structured datasets, however, a knowledge graph is not automatically the best solution.
The crucial question is therefore not: ‘Can we represent our data as a graph?’, but rather: ‘Does linking this data help us make better use of our knowledge?’
How is a knowledge graph created?
What knowledge should be linked, and to what end?
For example, companies, products, services, people or topics.
For example, content → addresses → search intent.
Identical entities from different sources must be uniquely identified.
New data and relationships are continuously added and verified.
Conclusion: A knowledge graph turns data into connections
A knowledge graph organises knowledge as a network of entities and relationships. This is precisely why the concept is relevant to search engines, corporate knowledge and modern AI systems.
For SEO and GEO, this embodies a key fundamental principle: systems must not only be able to find content, but also classify brands, topics and contexts as unambiguously as possible.
Not every business needs its own knowledge graph. However, where complex information, semantic search or AI applications come together, it can turn isolated data into genuinely usable knowledge.
FAQ: Frequently asked questions about AI Mode
A knowledge graph represents knowledge as a network. Entities such as companies, people, or products are linked to one another through defined relationships.
Not necessarily. It is primarily a knowledge model. A graph database can serve as its technical foundation.
The Knowledge Graph maps knowledge and relationships. The Knowledge Panel is a visible information box in Google Search.
It highlights the importance of entities and relationships. For SEO, therefore, clear entity signals, structured data, and semantically clear content are relevant.
It can provide structured context to AI systems and, in particular, complement RAG or GraphRAG architectures.
