What Is a Knowledge Graph in AI? (2026): Entities, Links, and a Query

A knowledge graph is a set of entities and the relationships between them, stored so you can ask a precise question and get the linked facts back. A person, a company, a product, and a policy are entities. “Works at,” “replaces,” and “requires” are relationships. The graph is the map. A language model is one way to ask the map a question in a sentence.

A pile of documents searched by similarity is a different object. That search is what embeddings are and, when you store them for lookup, what a vector database is. Answering from the passages you found is what RAG is. A graph answers from a link you stored. RAG answers from a paragraph the search returned.

Published October 24, 2026. This page does not rank graph databases.

Graph, search, and a wiki

Store	What a question hits	A question it is good at	A question it is weak at
Knowledge graph	An entity and its links	“Which plans include SSO, and which tool is the system of record?”	A paragraph of advice that was never turned into a link
Vector search plus a model	Passages that look similar	“What does the refund article say?”	A count across entities, unless you also compute it
A wiki	A page a person wrote	The explanation, with an owner	A join across fifty pages
The model’s training	Patterns in text	A definition, with a source you still open	Your price list from last Thursday

flowchart TD
  facts["You name entities and the links you trust"] --> graph["The graph stores those links"]
  ask["Someone asks in a sentence"] --> route{"Is the answer a link, or a passage?"}
  route -->|A link| graph
  route -->|A passage| rag["Search the documents"]
  graph --> cite["Show the entities"]
  rag --> cite2["Show the passage"]

Teams combine them. The graph holds the structured facts: account, plan, owner, renewal date. The documents hold the policy prose. The model writes the sentence only after one of those stores returns something a person can open. If neither returns it, the honest reply is that the fact is missing. That refusal is the difference between a graph-backed answer and what an AI hallucination is.

What the directory tools actually are

Notion AI searches and drafts across a workspace. The full agent experience is on Business at $20 per seat per month, on the directory card checked October 16, 2026. A Notion database with relations is closer to a small graph than a blank page is. It is still a workspace. Calling it a knowledge graph does not add a query engine you did not configure. The seat is Notion AI pricing explained.

Perplexity searches the public web and shows links. Pro is $17 a month billed annually on that same October 16 card. It is retrieval over pages, which is retrieval-augmented chat. It is not your private graph of accounts.

Chatbase answers from files you upload. Hobby is $32 a month billed yearly, 700 message credits. Upload a policy. You get passages. You do not get a graph unless you build the entities somewhere else and keep them updated.

Hex is a notebook and a semantic layer over data you already warehouse. Professional is $36 per editor per month on the pricing page read October 24, 2026. A semantic model is a governed set of definitions. It rhymes with a graph when the definitions are entities in tables. It is still SQL under the chart. The invoice is Hex pricing explained.

None of those products replaces the work of deciding which relationships are true. A graph that is wrong is a confident join. Someone has to own “this account uses this plan.” The model can propose the link. A person, or a system of record, confirms it.

When the graph is the wrong project

Build one when the questions are about relationships you will ask again: which customers are on which plan, which articles cite which feature, which vendors feed which workflow. Skip it when the questions are “summarize this PDF.” That is a document and a chat. Claude Pro at $20 a month, or $17 billed annually, is enough for the PDF. The directory recorded those prices on October 16, 2026.

If the relationship already lives in a CRM or a warehouse, query that system. Copying it into a second graph creates two sources of truth. The model will answer from the one you connected, including the stale one.

FAQ

Is a sitemap a knowledge graph?

No. A sitemap is a list of URLs. A graph needs typed entities and relationships you can query. A blog can describe both. They are not the same store.

Do I need a graph for a chatbot?

You need a source the bot must stick to. For most help centers that source is articles, and the method is retrieval. Add a graph when the answer depends on a relationship the articles do not state in one place.

Can a language model be the graph?

It can talk as if it were. The links are not inspectable, and they go stale. Store the links. Let the model phrase the question and the answer.

How is this different from embeddings?

Embeddings find text that is similar. A graph returns a relationship you recorded. Use embeddings to find the paragraph. Use the graph to answer “who is connected to whom.”

Last verified

October 16, 2026 for Notion, Perplexity, Chatbase, and Claude prices in the directory. October 24, 2026 for Hex’s $36 Professional seat, from hex.tech/pricing. Published October 24, 2026.

Sources: Notion AI, Perplexity Pro, Hex pricing.

Published by Andrew for AI Tools Optimizer.

Perplexity is in the research directory. Directory pages may use affiliate links. A purchase through them may earn us a commission at no extra cost to you.