Preview release. RDF Studio is under intensive active development and is provided as-is — not intended for production or business-critical systems; evaluate it on test data. Expect occasional rough edges, and expect them to be fixed fast. The core workflows are solid and usable today; some advanced features are still maturing.
Building knowledge graphs shouldn't require a PhD in description logics. But most teams face a frustrating choice: use a powerful-but-intimidating desktop ontology editor, or fall back to generic tools that lack semantic awareness entirely. Ontology experts work in isolation, data engineers avoid RDF altogether, and the team never reaches the shared understanding that makes knowledge graphs valuable.
RDF Studio occupies the space between these extremes — a unified workspace where ontologists, data engineers, data analysts, and knowledge graph engineers work together on the same data, each at a level of abstraction that fits their role.
All these roles work in a single application, on the same data, with shared context — no tool-switching, no file exports, no coordination overhead.
Not more features — more accessible features.
Ontology-driven everything. The ontology is the backbone of every feature: Browse generates faceted filters from property types, the SPARQL editor autocompletes with your class and property IRIs, the graph visualizer colors nodes by rdf:type, and SHACL shapes are generated from your OWL axioms. No ontology yet? RDF Studio discovers classes and structure directly from your data as a starting point — an ontology unlocks the complete experience. Change the ontology and every other module reflects it immediately.
rdf:type
Context-aware AI with guardrails. AI suggestions appear as reviewable drafts on the canvas, highlighted in amber — never committed automatically. The AI reads your classes, properties, and instance patterns to make grounded suggestions. You accept, modify, or discard each one through the UI. AI proposes; you decide.
Advanced visualization, zero configuration. Most graph databases ship basic bubbles-and-edges views. Anything richer requires SDKs, developer effort, and custom code. RDF Studio ships it ready to use: 8 layout engines, node expansion with predicate selection, edge and node grouping with aggregated counts, visibility filters by type, entity-aware coloring with 12 color pairs, 40+ automatic icon assignments, and graph search. No dev kits, no SDKs, no custom development required.
One tool, every role. The semantic workflow traditionally demands a desktop ontology editor, a triple store admin UI, a separate SPARQL client, and a visualization tool — each with its own learning curve. RDF Studio unifies them into one workspace.
No vendor lock-in. Your ontologies, data, and metadata are standard Turtle files exportable at any time, loadable in any tool. Standards supported: RDF/OWL, SPARQL 1.1, SHACL, Turtle, Protégé-compatible.
RDF Studio is designed as a modern alternative to traditional desktop ontology editors — in the cloud or as a self-contained desktop app. It delivers full OWL 2 DL development alongside live data, built-in validation, AI assistance, and a visual SPARQL environment — all in one integrated workspace.
Full OWL 2 DL, zero setup. Create and edit ontologies visually by linking nodes on the canvas. Define object properties, data properties, and complex OWL 2 axioms without writing Turtle. A one-click HermiT consistency check detects unsatisfiable classes, disjointness violations, and logical contradictions — the same reasoning power as Protégé's reasoner, built in.
Multi-ontology canvas. Load domain, reference, mid-level, and upper ontologies side by side on the same canvas. Map your classes to external IRIs, identify overlaps, and align your taxonomy to industry standards — visually.
Draft/commit workflow. Edit drafts without touching production. Commit when you're ready, or discard and start over. Maturity scoring tracks ontology quality across completeness, documentation coverage, and structural correctness.
AI-assisted authoring. Upload documents or domain descriptions to generate a first draft — classes, properties, relationships, and competency questions. Every AI proposal is a reviewable draft; nothing commits without your approval.
SHACL shapes from OWL. Generate a full SHACL shapes library automatically from your OWL 2 ontology, or author shapes manually in the visual shape builder. Live validation runs on every instance edit with a severity dashboard and bulk-fix suggestions.
An excellent lightweight alternative to Protégé for teams that want OWL authoring, live data querying, SPARQL, and graph visualization in the same tool — without a heavyweight toolchain or a separate triple store to manage.
RDF Studio ships a full-featured training environment so you can learn by doing — not just reading.
The centrepiece is the Northwind Knowledge Graph — a mature, Protégé-compatible OWL 2 ontology built on the classic trading-company domain, with a full SHACL shapes library and the most comprehensive SPARQL query collection of any open RDF training resource:
Every query in the library is tagged, explained, and executable with one click. Browse by category (Basic → Advanced → Analytics) or by topic (joins, OPTIONAL, aggregation, inferencing, SHACL).
Additionally, RDF Studio ships built-in articles, guides, and reasoning guides covering OWL constructs, SHACL design, SPARQL patterns, and semantic web concepts — all readable inside the app without opening a browser tab.
The semantic data tooling space divides into four tiers:
Desktop ontology editors (Protégé and equivalents) give maximum OWL 2 control but are isolated editors. You design your ontology, export it, load it into a separate triple store, and use another tool to query data. The feedback loop is slow, and most team members never touch the ontology editor.
Enterprise knowledge graph platforms combine ontology management, data integration, SPARQL, visualization, and governance — but with enterprise-grade complexity, heavyweight deployments, steep onboarding curves, and significant licensing costs. Built for organizations with dedicated KG teams.
Vendor-specific triple store UIs are excellent within their ecosystem, but lock you into a vendor and focus on querying and administration rather than ontology design.
Lightweight / educational tools (WebVOWL, YASGUI, Jupyter) solve specific problems well but don't provide an integrated workflow.
RDF Studio doesn't beat the specialized incumbents on every single feature — it delivers the same core workflow through a clean, modern interface the whole team can use from day one.
* Ontology review, approval and version history — including a triple-level diff between versions — are available today. ** More advanced visualization features are on the roadmap. *** The built-in Oxigraph store plus dedicated connectors for GraphDB, Stardog, RDFox and RDFLib; any other SPARQL 1.1 endpoint is supported via the generic connector. The columns are broad, representative product categories, not specific products. Capabilities differ between products within a category — "Varies" means a capability depends on the specific product. The comparison reflects our understanding of typical offerings and may not be current for every product.
* Ontology review, approval and version history — including a triple-level diff between versions — are available today. ** More advanced visualization features are on the roadmap. *** The built-in Oxigraph store plus dedicated connectors for GraphDB, Stardog, RDFox and RDFLib; any other SPARQL 1.1 endpoint is supported via the generic connector.
The columns are broad, representative product categories, not specific products. Capabilities differ between products within a category — "Varies" means a capability depends on the specific product. The comparison reflects our understanding of typical offerings and may not be current for every product.
RDF Studio's AI operates within the context of your project — constrained by your ontology, your data, and your workflows. It provides assistance, but within guardrails that keep humans in control.
When AI assists, it has access to your class hierarchy, property definitions, actual instances, and project namespaces — so suggestions are grounded in your model, not hallucinated. AI-proposed changes appear highlighted in amber on the visual graph editor, marked with the AI icon, so provenance is always clear. Nothing reaches your production ontology until you explicitly commit the draft.
The principle is consistent: AI proposes, you dispose.
RDF Studio is a powerful semantic data workspace — with deep capabilities already in production and a fast-moving roadmap.
What works today: Faceted data exploration, visual ontology authoring with full OWL 2 DL (HermiT consistency checking), 43-rule OWL 2 RL reasoning via a native Rust engine, SHACL validation that runs inside the storage engine and passes the complete W3C SHACL Core test suite, a query engine that plans joins by cost from real statistics and shows you the plan it chose, a visual SPARQL Composer, an advanced graph visualizer (8 layouts, grouping, expansion — no dev effort required), AI-assisted ontology editing and SPARQL generation, a first ontology built automatically from your own database schema and sample data — with SHACL shapes, source-to-model mappings and a provenance record produced alongside it — a complete Northwind training environment, and a native desktop app (macOS, Windows, Linux — fully offline, no server or internet connection required).
Starting from what you already have. Point RDF Studio at a schema script, a catalog extract, or a folder of CSV, Excel, JSON or Parquet files, and it works out the model itself: tables become classes, columns become properties with the types the source declares, required columns become required properties, and foreign keys become relationships. Everything a schema can state is computed rather than guessed, and each term records which of the two it was — so you can see at a glance what came from your database and what was inferred from a sample. Documents you supply alongside are read for meaning, and what they add never overrides what the source declared. Where your schema and your data disagree, it tells you rather than quietly picking one.
Built-in Oxigraph store handles real ontology development, datasets in the hundreds of millions of triples, and full OWL 2 RL reasoning today. Dedicated connectors for GraphDB, Stardog, RDFox and RDFLib — plus a generic SPARQL 1.1 endpoint connector — let you work with larger external deployments, so the store you point it at sets the ceiling, not the tool.
On the roadmap: Scheduled data integration pipelines and graph analytics. (Ontology review and approval workflows shipped and are available today.)
RDF Studio is a preview of a mature knowledge graph platform. For teams building their first KG or learning RDF/OWL, it delivers immediate, hands-on value that no other tool offers in such an integrated, approachable package. For teams that need enterprise-scale governance workflows today, RDF Studio complements existing tools — use a desktop editor for deep modeling, an enterprise platform for production pipelines, and RDF Studio for day-to-day exploration, prototyping, learning, and team collaboration.
You can start using RDF Studio right now:
