# Export formats

> Export your ontology as JSON, Markdown, CSV, PDF, AI Agent Bundle, or RDF/OWL (Turtle and JSON-LD) — and when to use each.

**Category:** Import & Export | **Tab:** features

---

Export your ontology in multiple formats, each optimized for different tools and workflows.

## JSON

Structured data export for programmatic use and AI tool configuration.

**What's included:**
- All concepts with their classes and descriptions
- All terms with variants, status, and market availability
- Semantic relationships between concepts
- Market definitions and constraints

**Best for:** MCP tool configuration, API integration, TMS import, programmatic access.

## Markdown

Human-readable export with clear headings, tables, and structured prose.

**What's included:**
- Concepts organized by class with descriptions
- Terms per locale with preferred term, variants, and status
- Market availability overview
- Relationship summaries

**Best for:** LLM prompts, Gemini, Custom GPTs, internal documentation, knowledge sharing.

## CSV

Flat tabular data with configurable columns and two layout options.

**Layout options:**

- **Translation Grid** — Wide format with one row per concept and markets as columns. Best for spreadsheet review and side-by-side comparison across locales.
- **Detailed Rows** — Long format with one row per target term. Best for translation management and per-term filtering.

You can configure exactly which columns to include and in what order using the CSV column configurator.

**Best for:** Spreadsheets, translators, bulk editing, data review.

## AI Agent Bundle (ZIP)

A structured folder of Markdown files with a top-level `CLAUDE.md` entry point, optimized for AI coding agents.

**What's included:**
- `CLAUDE.md` — Agent entry point with ontology overview and navigation instructions
- Per-class folders with concept definitions
- Per-locale term files with variants and usage context
- Market definitions and relationship data

The AI Agent Bundle is designed for the **GitHub Sync → Claude Projects** workflow: sync your ontology to a GitHub repo, then attach it as a Claude Project for context-aware AI assistance.

**Best for:** Claude Projects, Claude Code, AI coding agents, developer workflows.

## RDF/OWL — Turtle and JSON-LD

Standards-based semantic web exports built on **SKOS-XL + OWL**, for graph databases, ontology tooling, and linked-data publishing.

Both files describe **exactly the same graph** — the same concept IRIs, the same labels, the same predicates. The only difference is serialization, so pick whichever your consuming tool reads most naturally.

### Turtle (`.ttl`)

Compact, human-readable RDF. The default interchange format for the semantic web stack.

**Choose Turtle when:**
- You are loading into a triple store or graph database — GraphDB, Stardog, Neo4j (via n10s), Apache Jena, Virtuoso
- You will query the data with SPARQL
- You are opening the ontology in an editor such as Protégé or TopBraid
- A reviewer needs to read the raw model by eye — Turtle is by far the most legible RDF syntax

### JSON-LD (`.jsonld`)

The same RDF expressed as JSON, with a `@context` that maps the short keys onto the full vocabulary.

**Choose JSON-LD when:**
- The consumer is a web or JavaScript pipeline that already parses JSON
- You are storing the graph in a document database, search index, or vector store alongside other JSON
- You need structured data for the web, or a procurement/tender checklist asks for a machine-readable linked-data format
- Your team is comfortable with JSON but not with RDF syntax — JSON-LD degrades gracefully to plain JSON

### What's included in both

Concepts as `skos:Concept`, source and target terms as `skosxl:Label` per locale, classes as `skos:ConceptScheme`, and semantic relationships as typed `owl:ObjectProperty` predicates. Black Ice-specific predicates (market, status, availability, allowed and forbidden variants, translation policy) live under the published namespace `https://black-ice.ai/ns#`, which is dereferenceable at `/black-ice-ns.ttl`.

Predicate names are frozen for the lifetime of schema version `1.0`, so downstream consumers can safely pin to the namespace.

**Best for:** GraphDB, Neo4j (via n10s), Stardog, Protégé, SPARQL pipelines, RAG grounding, public-sector and linked-data publishing.

> RDF/OWL exports are available on **Pro and Builder**.

## PDF

Prose document with formatted headings, tables, and structured content for document-based AI tools.

**What's included:**
- Full ontology overview with concepts organized by class
- Term tables per locale
- Market availability summaries
- Relationship descriptions

**Best for:** NotebookLM & document-based AI.

## Knowledge graph images (PNG, SVG, PDF)

Separate from the data exports above, the **Knowledge Graph** view exports the visual graph itself from its own export menu.

- **PNG** — standard, retina (2×) and print (4×) resolutions
- **SVG** — vector, scales to any size and stays editable in design tools
- **PDF** — vector page, ready to drop into a document

Retina PNG, print PNG, SVG and PDF are available on **Pro and Builder**; standard PNG is available on every plan.

**Best for:** Slides, board decks, architecture documentation, onboarding material, client presentations.

## JSON Schema

The export dialog also offers the **JSON Schema** that describes the JSON export — copy it to the clipboard or download it as a file.

**Best for:** Validating exported files in a pipeline, generating typed clients, and documenting the contract for engineering teams.

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## Where each format comes from

| Surface | Formats produced |
|---|---|
| **Export Ontology dialog** (in app) | JSON, Markdown, CSV, AI Agent Bundle, PDF, Turtle, JSON-LD, JSON Schema |
| **Knowledge Graph view** (in app) | PNG, SVG, PDF images of the graph |
| **GitHub Sync** | AI Agent Bundle — Markdown files written into your repository |
| **Workspace API and MCP** | JSON responses from `GET /query-ontology` and the MCP read tools |

RDF/OWL, PDF and graph images are produced in the app rather than through the API. If you need them on a schedule, export from the app and commit the file, or sync the Markdown bundle to GitHub and convert downstream.

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## Which format should I use?

Use this table to find the right format for your tool or workflow:

| Tool / Workflow | Recommended Format |
|---|---|
| **Gemini** | Markdown |
| **Custom GPTs** | Markdown |
| **Claude Projects** | AI Agent Bundle (ZIP) |
| **Claude Code** | AI Agent Bundle (ZIP) |
| **NotebookLM** | PDF |
| **MCP tools** | JSON |
| **Spreadsheet review** | CSV (Translation Grid) |
| **Translation management** | CSV (Detailed Rows) |
| **API / TMS integration** | JSON |
| **Internal documentation** | Markdown or PDF |
| **Graph databases / triple stores** | Turtle |
| **SPARQL queries** | Turtle |
| **Ontology editors (Protégé, TopBraid)** | Turtle |
| **Web / JavaScript linked-data pipelines** | JSON-LD |
| **Public-sector tenders and RFPs** | JSON-LD (or Turtle, if specified) |
| **RAG grounding for LLMs** | JSON-LD or Turtle |
| **Slides and presentations** | Knowledge graph PNG or SVG |
| **Validating exports in CI** | JSON Schema |

> **Tip:** If you're using GitHub Sync, the AI Agent Bundle format is automatically optimized for your synced repository structure — making it ideal for Claude Projects and AI-assisted development.