Pages with meaning.
Bring readable explanations and structured facts together. Define entities, properties, and relationships without separating the story from the data.
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Your knowledge deserves
more than a folder.
Write a page. Give it meaning. Follow the relationships.
The same knowledge, useful in entirely new ways.
Write like a human.
Pages, explanations, and shared understanding. The wiki experience people already know.
Connect like a graph.
People, projects, evidence, and ideas—with explicit meaning between them.
Reason with context.
Make knowledge useful to search, queries, and AI without losing its sources.
Ask. Query.
Explore.
Start with a question, work with the data, or follow a connection. All in the same wiki.
Everything a wiki
should be.
One coherent system for human knowledge and machine understanding. Create, connect, query, and reuse your knowledge in the same workspace.
Built to be built together.
A familiar MediaWiki foundation for shared authorship, page histories, discussion, and knowledge that keeps improving.
Ask better questions.
Go beyond matching words. Combine semantic discovery, graph exploration, and SPARQL over connected facts.
Keep the “how do we know?”
Make sources, context, and change history part of the knowledge itself. Inspect the evidence behind a claim, not just the claim.
Give AI something to go on.
Source-grounded retrieval, reusable context, and explicit relationships support neurosymbolic AI and inspectable reasoning.
Your knowledge.
Your terms.
Open standards, portable data, and deployment in your own environment. Knowledge should outlive any model, vendor, or interface.
A better interface.
A deeper foundation.
The wiki made knowledge collaborative.
The graph makes it connected.
GraphWiki brings those ideas together.
MediaWiki at the foundation
A mature ecosystem for collaborative knowledge, with familiar pages and a long history of extensibility.
A graph at the heart
A triplestore-based architecture models meaning explicitly and makes connected knowledge directly queryable.
AI with something to reference
Machine-usable knowledge stays inspectable and independently updateable.
Made for the way
your world connects.
From a single research group to an organisation’s shared memory. Start with the knowledge that matters to you.
Research & science
Connect questions, methods, datasets, and findings. Keep the evidence within reach of the next discovery.
Connect your research knowledgeTeams & organisations
Link people, decisions, services, and processes. Build institutional memory that survives a change of team.
Connect your organisationEngineering & operations
Connect systems, requirements, dependencies, and incident knowledge. Trace the impact of a change and keep operational context close to the work.
Connect your systems knowledgeBuilt natively on
W3C standards.
GraphWiki writes current page content and structured statements directly to an RDF triplestore as you edit. Query that same knowledge with SPARQL, built into the wiki.
Native graph storage and querying. Out of the box.
Native graph storage & querying
Current page content and statements are RDF. Read and write the graph through standard SPARQL endpoints.
Shared vocabularies
Describe concepts, organisations, datasets, provenance, data quality and usage policies with shared RDF vocabularies.
Ontologies, shapes & context
Describe your domain with RDF models. Your graph tooling supplies the inference and shape validation you choose.
Open exchange & publishing
Connect to RDF’s standard serializations and web publishing formats through your triplestore and connected tools.
Open connections for AI & APIs
Connect agents through SPARQL and wiki APIs. Describe integrations with OpenAPI and JSON Schema; connect MCP clients through a compatible adapter.
An accessible web
Semantic HTML, keyboard navigation and assistive-technology support. WCAG 2.2 AA is our accessibility target.
Our accessibility approach
W3C standards form the graph foundation, alongside open vocabularies and protocols from the wider web. Formats, reasoning, validation and AI adapters follow the capabilities of your connected tools.
Good questions.
Clear answers.
Something else on your mind?
Talk to the team
What is GraphWiki?
GraphWiki is Dobriy AI’s semantic wiki: a familiar collaborative workspace with a triplestore-based architecture for structured, connected knowledge. People can read and maintain it; software and AI can query and reuse it.
How do I get started?
Contact the Dobriy AI team with your use case, your existing wiki or data, and your hosting requirements. We’ll help you set up GraphWiki for your team and connect the knowledge you already have.
How is this different from a regular wiki?
A regular wiki mainly connects documents through links. A semantic wiki also describes what things are and how they relate: a project uses a dataset, a person leads a team, a claim cites a source. GraphWiki makes those explicit relationships a natural part of everyday wiki work.
How does it relate to Semantic MediaWiki?
GraphWiki builds in the MediaWiki ecosystem and shares the semantic-wiki ambition of structured, queryable knowledge. It combines a coherent user experience, a triplestore-based architecture, and knowledge that is useful to AI. We assess your existing extensions and data model when planning a migration.
How do AI, RDF, and SPARQL work together?
RDF represents your knowledge as connected facts. SPARQL lets you query those relationships directly. AI uses the same structured knowledge for source-grounded retrieval and reasoning, keeping the underlying evidence available to inspect.
Can we bring an existing wiki or host it ourselves?
Yes. GraphWiki supports existing MediaWiki knowledge and deployment in your own environment. We’ll assess your extensions, data model, access requirements, and infrastructure to plan the right migration and hosting setup.
Is GraphWiki the same as Dobriy Graph?
No. GraphWiki is the collaborative knowledge product. Dobriy Graph is the related platform for hosting and querying knowledge graphs. They have complementary roles: author and organise knowledge in a wiki, then make structured data useful across a wider ecosystem.
Let’s make it
mean something.
Have a wiki that needs to grow up?
A knowledge base that deserves a better foundation?
Bring it together with GraphWiki.
Opens your email app. Or write to office@dobriy.ai.