The knowledge graph feature extracts named entities (people, organizations, places, concepts) from your vault documents and builds an interactive 3D visualization of their relationships.

## Prerequisites

- A [document vault](/guides/first-vault/) with at least one imported document
- Enough free memory for the NER model (183 MB–583 MB depending on model choice)

## Enable the knowledge graph

1. Open the vault overlay and select your vault.
2. Click the **Knowledge Graph** toggle to enable it.
3. Daneel downloads the NER (Named Entity Recognition) model if not already cached, then processes all documents in the vault.

Entity extraction runs locally in a dedicated web worker using GLiNER (an ONNX model). No data leaves your browser.

## Choose a NER model

Open **Settings > Knowledge Graph** to select a model:

| Model | Size | Languages | Notes |
|-------|------|-----------|-------|
| GLiNER Small v2.1 (fp32) | 583 MB | English | Highest accuracy |
| GLiNER Small v2.1 (int8) | 183 MB | English | Good balance of size and quality |
| GLiNER Multi v2.1 (int8) | 349 MB | Multilingual | For non-English documents |
| GLiNER Multi v2.1 (fp16) | 580 MB | Multilingual | Highest multilingual accuracy |

The int8 English model is a good default for most use cases.

## Pick an ontology preset

Ontology presets define what types of entities Daneel looks for. Choose one in **Settings > Knowledge Graph**:

- **General** — people, organizations, places, events, concepts
- **Academic** — researchers, institutions, theories, publications
- **Legal** — cases, statutes, courts, parties
- **Medical** — conditions, treatments, drugs, anatomy
- **Programming** — languages, frameworks, APIs, data structures
- **Business** — companies, products, markets, financials
- **Travel** — destinations, landmarks, transport, accommodations
- **History** — historical figures, battles, treaties, eras

You can also define a **custom ontology** by entering your own entity type labels.

## Explore the visualization

Once extraction completes, the knowledge graph appears as a 3D interactive visualization:

- **Nodes** represent entities, sized by how often they appear
- **Edges** represent co-occurrence relationships between entities
- **Colors** indicate entity types (people in blue, organizations in green, places in red, etc.)
- **Hover** over a node to see its label and type
- **Click a node** to focus on its neighborhood and trigger a Wikipedia lookup
- **Click and drag** to rotate the view
- **Scroll** to zoom in and out

For analytics, path finding, sizing modes, topic clusters, and the Wikipedia lookup, see [How to Explore Your Knowledge Graph](/how-to/explore-knowledge-graph/).

## Customize the visualization

In **Settings > Knowledge Graph**, adjust:

- **Particle animation** — toggle animated particles along edges
- **Bloom glow** — toggle a glow effect on nodes
- **Charge strength** — how strongly nodes repel each other (affects spacing)
- **Link opacity** — transparency of relationship edges
- **Node scale** — base size multiplier for nodes

## How entity resolution works

Daneel automatically deduplicates entities using normalized string matching with a configurable threshold (default: 85% similarity). "OpenAI", "Open AI", and "OPENAI" resolve to a single entity. Adjust the deduplication threshold in settings if you need stricter or looser matching.

## Next steps

- [Explore the graph with analytics, path finder, and Wikipedia lookup](/how-to/explore-knowledge-graph/)
- Learn more about [what knowledge graphs are and why they help](/concepts/knowledge-graph/)
- Understand the [graph analytics layer](/concepts/graph-analytics/) — importance, topics, bridges, paths
- See the [Settings reference](/reference/settings/) for all knowledge graph parameters
- [Build a Document Vault](/guides/first-vault/) if you haven't created one yet