---
title: "What is a knowledge graph?"
description: "How extracted entities and relationships turn documents into a map you can navigate."
canonical: https://ocho.bot/docs/market-comparison/what-is-a-knowledge-graph
last_updated: 2026-08-21
---

# What is a knowledge graph?

> How extracted entities and relationships turn documents into a map you can navigate.

A knowledge graph is a map of *things* and *how they relate*: the people, products, materials, projects, and concepts in your documents (the nodes), and the relationships between them (the edges). Instead of a pile of files, you get a structure you can navigate — "show me everything connected to this alloy," "which projects touch this regulation?"

## Hand-drawn vs. extracted

Most tools that show you a "graph" are visualizing links you made yourself. Obsidian's famous graph view maps the `[[wiki-links]]` you typed by hand — if you never linked two notes, no connection exists, no matter what the notes say. That's a map of your linking discipline, not your knowledge.

An **extracted** knowledge graph is built by AI reading the document content itself: it identifies entities, infers relationships, merges duplicates ("AI" and "artificial intelligence" become one node), and scores its confidence. Connections you never noticed show up because they're in the text, not because someone remembered to add a link.

## Why it matters for retrieval

Keyword and semantic search find passages that *look like* your query. A graph answers a different class of question — the connective ones: everything about an entity across hundreds of documents, the chain between two concepts, the clusters in a corpus you've never read end-to-end. Paired with search, the graph also gives AI answers richer context about how facts relate.

The payoff is measured, not hypothetical: on entity-rich corpora, graph-based retrieval produces **3.4× more comprehensive answers** than vanilla RAG ([Microsoft Research, GraphRAG](https://arxiv.org/abs/2404.16130)) — and the connective questions are exactly where plain search underperforms.

## How Ocho does it

- **Automatic.** When a search-enabled dataset finishes building, graph

extraction runs on its own — entities, relationships, confidence scores,   and community clusters. No setup step.

- **Self-maintaining.** When documents change, the graph flags itself stale

and updates. Chat stays available the whole time.

- **Explorable.** Every dataset gets an interactive graph: pan, zoom, click an

entity to see its relationships and the exact source excerpts it came from,   and jump straight to the cited page.

- **Wired into everything.** Search results light up on the graph, citations

show a mini-map of related entities, and saved views share a vantage point   with your team.

See it in action: [Explore the knowledge graph](/docs/search/explore-the-knowledge-graph).

All docs: https://ocho.bot/docs

---

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