What is a Knowledge Graph?

Connecting Knowledge Instead of Searching Knowledge

In companies, law firms, or research institutions, knowledge is held in many minds, files, and databases – but rarely in a way that machines can understand.
A Knowledge Graph changes this: It makes knowledge connectable, traceable, and intelligently usable.

What is a Knowledge Graph?

A Knowledge Graph is a digital map of knowledge.
It describes knowledge not as tables or lists but as relationships between things.

A graph consists of:

• Nodes (Entities) → e.g., “Product,” “Customer,” “Paragraph”

• Edges (Relations) → e.g., “belongs to,” “refers to,” “is part of”

This structure not only shows what exists but also how it is interconnected.

Example:

Product X is made by Supplier Y,
meets Standard Z,
and is the successor of Product W.

This way, a computer can not only retrieve data but also understand context.

The Difference from a Database

In a traditional database, data is structured but isolated:
Each row represents a record, and each column represents an attribute.
This is efficient, but semantically “blind.”

A Knowledge Graph, on the other hand, thinks in relationships:

Not just “Product 123 has price 50”,
but “Product 123 is based on technology X, which is defined in Standard Y”.

This allows AI systems to logically infer, recognize connections, and provide explanations, rather than just producing result lists.

Why Knowledge Graphs Are Important for Companies

• They make knowledge findable and context-related.

• They create a shared knowledge base across departments.

• They are the foundation for explainable AI systems because they not only store knowledge but also structure it.

A Corporate Knowledge Graph (CKG) goes one step further:
It represents the entire corporate knowledge in such a graph – from documents to processes to technical terms.
This creates a digital brain that not only contains information but understands it.