What is a knowledge graph?

A knowledge graph is a connected collection of facts about things and how they relate, organized by a schema so software can query and cite it.

Concept

A knowledge graph is a collection of facts about things and how they relate, typically organized by a schema, a shared list of allowed types. Think of it as a company’s memory drawn as a map: customers, products, and support tickets are the points, and facts such as “this ticket is about the export feature” are the lines between them. Because each fact can also record where it came from, software can answer questions by following connections and point to its sources. That is why knowledge graphs are used to ground search, recommendations, and large language model (LLM) applications, such as chatbots, in connected, checkable facts.

Learning objectives

After reading this article you will be able to:

• Describe what a knowledge graph contains, from entities to sources
• Explain how a knowledge graph differs from a graph database
• Outline how to build one, including extraction and entity resolution
• Describe how to keep a knowledge graph current as sources change

What is in a knowledge graph?

A knowledge graph holds things (entities), the named connections between them (relationships), details about both (attributes), and a record of where each fact came from (provenance). A schema or ontology sets the rules for which types are allowed.

Part	What it is	Example
Entity	A distinct thing the graph knows about	A customer, a product, a feature, a support ticket
Relationship	A typed, directed connection between two entities	A ticket is ABOUT a feature
Attribute	A value that describes an entity or relationship	A product’s releaseStage, a relationship’s confidence
Ontology or schema	The allowed entity types, relationship types, and attributes	”A Ticket can be ABOUT a Feature, not a Customer”
Provenance	Where each fact came from and when	The document chunk a relationship was extracted from

For example, a support team’s knowledge graph might connect a customer, the ticket they opened, the feature it is about, and the guide that documents that feature:

A schema or ontology is usually what separates a knowledge graph from an arbitrary set of links. It fixes what kinds of things exist and how they may relate, so facts from different sources land in the same shape and queries mean the same thing everywhere. An ontology usually goes further than a schema: it can also define class hierarchies (a Laptop is a kind of Product), constraints, and inference rules that derive new facts.

What is the difference between a knowledge graph and a graph database?

A knowledge graph is the data; a graph database is software that can store and query it. It is like the difference between a city map and the app you view it in. The two are often used together, but they are not the same thing.

	Knowledge graph	Graph database
What it is	A body of facts organized by an ontology	A storage and query engine
Main concern	Meaning, consistency, and provenance of facts	Storage, indexing, transactions, and traversal
Can exist without the other?	Yes, for example as RDF files or relational tables	Yes, for example holding a network topology or social graph

A knowledge graph can be stored as a property graph, where nodes and edges carry their own properties, or as RDF triples, three-part subject-predicate-object statements. See property graph vs RDF for how the two models differ.

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How do you build a knowledge graph?

Decide which types of things and connections matter, pull facts out of your sources, merge duplicates, record where each fact came from, and load the result into a database.

1. Define the ontology

   List the entity types, relationship types, and required attributes your questions need. Start small; a focused schema is easier to extract into and to query.

2. Collect sources

   Structured records, such as account and product tables, map to entities almost directly. Unstructured sources, such as documents, tickets, and conversations, need extraction.

3. Extract entities and relationships

   For unstructured text, the application runs an extraction pipeline. It splits each document into chunks, asks an LLM or another extraction model to return the entities and relationships it finds in a fixed format that matches the ontology, and rejects output that does not validate against the schema.

4. Resolve entities

   Entity resolution decides when two mentions refer to the same thing. One ticket says “the billing page” and another says “the invoices screen”; both may mean one feature. Typical techniques combine exact identifiers, normalized names, embedding similarity to find candidates, and rules or human review to confirm a merge.

5. Record provenance

   Link each extracted fact to the chunk and document it came from, with the extraction time and a confidence value. Provenance supports citations, audits, and later corrections.

6. Load, index, and query

   Write entities, relationships, and provenance together, and add indexes for the lookups and searches your application runs.

How do you keep a knowledge graph fresh?

Treat it as an ongoing maintenance loop, not a one-time load. For example, when someone edits a page on the company wiki, you re-extract that page rather than reloading the whole wiki.

• Ingest incrementally. Track which facts came from which source, and re-extract only the documents that changed.
• Supersede rather than overwrite. Mark outdated facts with validity fields such as validTo or isLatest, and link a new version to the one it replaces, so history stays queryable.
• Retract by source. When a document is deleted or corrected, provenance tells you exactly which facts to remove or re-check.
• Re-run entity resolution. New mentions can reveal that two existing entities are the same, or that one entity should be split.

The same patterns apply to long-term memory for AI agents, where an agent’s facts about a user change over time.

How do knowledge graphs help LLMs and RAG?

A knowledge graph lets an AI application retrieve connected facts, not just passages that sound like the question. That helps it answer questions that span several facts and cite its sources.

Retrieval-augmented generation (RAG) gives an LLM retrieved context before it answers. Retrieval based only on vector similarity, which finds text with similar meaning, struggles with a question such as “which customers reported problems with features in the Reports product?” The answer is spread across entities and relationships rather than contained in one passage.

Following explicit facts across the graph to answer it is an approach often called GraphRAG. Vector or keyword search, or a hybrid of both, typically finds the starting entities. LLM-extracted facts can be wrong, so keep confidence values and provenance, and let downstream prompts treat low-confidence facts accordingly.

How does HelixDB store a knowledge graph?

HelixDB stores data as one labeled property graph, which maps directly onto a knowledge graph:

• Entities and relationships. Entities are nodes and relationships are directed edges, each with exactly one label and typed properties. Because HelixDB is a multigraph, two sources asserting the same relationship can be kept as separate edges with their own properties.
• Embeddings and text on the same nodes. Store an embedding and a text field on an entity or chunk node, then add a vector index and a text index for similarity and BM25 search. The application computes the embeddings; HelixDB stores and indexes them.
• Provenance edges. Connect entities, or facts modeled as their own nodes, to their source chunks and documents with edges that carry properties such as confidence or extraction time. For a fact stored as a relationship edge, record provenance as properties on that edge, such as the source chunk ID, confidence, and extraction time. Indexes can target edge properties as well as node properties.
• Identity and freshness. Unique equality indexes on node labels suit canonical identifiers, and equality or range indexes on fields such as isLatest or validTo make queries that filter to current facts efficient.
• Scoped retrieval. Vector and BM25 search can be prefiltered to a traversal-defined candidate set, such as documents a user can read. See filtered vector search.

All entries in a write batch commit or roll back together, so an entity, its relationships, and their provenance can be written as one unit when they are sent in the same write batch. Start with the data model.

Frequently asked questions

What is the difference between a knowledge graph and an ontology?

An ontology is a formal specification of a domain: the types of entities and relationships and the rules that govern them, such as class hierarchies and constraints. The knowledge graph’s instance data, its entities and facts, follows the ontology, and many systems store the ontology in the graph alongside that data. One ontology can describe many knowledge graphs.

Do you need RDF to build a knowledge graph?

No. RDF is one standard way to represent a knowledge graph, and it suits publishing and integrating data across organizations. A labeled property graph works as well and is common for application-facing knowledge graphs.

Can an LLM build a knowledge graph automatically?

An LLM can extract candidate entities and relationships from text at scale, but the output needs validation against the ontology, entity resolution, and provenance. Treat extraction as one stage of a pipeline the application controls.

How is a knowledge graph different from a vector database?

A vector database retrieves items by how similar their embeddings are. A knowledge graph represents explicit, typed facts and how they connect. Many AI applications use both: vectors to find relevant starting points, and the graph to follow facts from there.

Related topics

What is a graph database?

Nodes, edges, traversals, and when a graph is the right model.

What is a property graph?

Labeled nodes and edges with typed properties on both.

Property graph vs RDF

Two ways to model a knowledge graph, compared side by side.

What is RAG?

Grounding a model’s answer in data retrieved at query time.

What is GraphRAG?

Retrieval that follows relationships, compared with vector RAG.

What is AI agent memory?

Working, episodic, and semantic memory for LLM agents.