Knowledge Graph
A structured representation of entities (people, products, places) and the relationships between them, stored as nodes and edges that software can query and reason over.
Definition
A knowledge graph stores facts as connections. Each node is a thing (a customer, a product, a regulation, a person) and each edge is a typed relationship between two things ("supplies", "owns", "is a version of", "reports to"). Facts are often written as triples: Eventify – developed by – Teksmobile. Because relationships are explicit, you can ask multi-hop questions that text search cannot answer directly, such as "which customers use a product that depends on a component from this supplier?".
Graphs and embeddings are complementary. A vector index finds passages that resemble a query; a graph follows precise links between entities. Modern "GraphRAG" approaches use an LLM to extract entities and relationships from documents into a graph, then retrieve from both the graph and the vector index when answering. Search engines use knowledge graphs for the entity panels beside results, which is also why structured data on websites matters.
Example: a manufacturer's graph links parts, suppliers, products and customers. When a supplier issues a recall, one query walks the graph from the affected part to every customer order that contains it.
Why it matters for your business
- Answers relationship questions that keyword or vector search handles poorly.
- Gives an LLM a precise, auditable source of facts instead of a loose set of passages.
- Building and maintaining the graph is real work; automated extraction needs review.
- Most organisations need it for a few high-value questions, not as a replacement for existing databases.
How Teksmobile uses it
We add graph retrieval to AI knowledge base projects when the questions demand it, typically in our knowledge management solution for organisations with complex product, contract or organisational relationships. The entity model is designed with the client's domain experts before any extraction is automated.
Related terms
Part of the Teksmobile AI Glossary. For longer reads, see our guides.
Frequently asked questions
Do we need a knowledge graph for RAG?
Not usually. Start with vector and hybrid search. A graph earns its place when questions depend on relationships across many documents, such as "which suppliers are affected if this part is recalled?".
What is GraphRAG?
A family of techniques that extract entities and relationships from documents into a graph, then use that graph alongside or instead of vector search to retrieve context for an LLM.
Which databases store knowledge graphs?
Dedicated graph databases (Neo4j and others), RDF triple stores, and increasingly relational databases with graph extensions. The choice depends on query patterns and the team's existing skills.