Turning Open Knowledge Into Public-Interest AI-Infrastructure: The Wikidata Embedding Project
As generative AI tools become the default way many people search for information, the question of where that information comes from matters more than ever. Wikimedia Deutschland’s Wikidata Embedding Project offers a concrete answer: it turns Wikidata’s vast, community-verified knowledge base into something small AI systems and humans can use to enable searches by meaning, not just by keyword. It demonstrates what public-interest AI infrastructure can and should look like. What it does The project applies vector-based semantic search to Wikipedia and its sister platforms’ existing data, covering nearly 120 million entries. Until now, Wikidata’s machine-readable data could only be accessed through keyword searches and specialised SPARQL queries — powerful, but difficult for smaller AI applications working with natural language and hard to learn for ordinary users. The new vector database changes that, and it also adds support for the Model Context Protocol (MCP), the emerging standard that lets AI systems communicate directly with external data sources. In practice, this means a query like “scientist” no longer returns just a keyword match — it surfaces related concepts such as nuclear scientists or Bell Labs researchers, translations, and linked images, giving AI systems the kind of contextual grounding that reduces hallucination and improves traceability. Built openly, with partners Wikimedia Deutschland developed the project in Berlin with Jina.AI and DataStax, and released it as a freely accessible public tool. It launched as a fully open and trasnparent alternative to the closed and intransparent datasets that power most commercial AI systems, giving developers a transparent, verifiable foundation to build on instead. It’s a working demonstration of what public-interest AI infrastructure can look like: multilingual, open, transparent and community-governed — proof that trustworthy AI doesn’t have to mean closed-off AI. How to get involved Developers can start building with… Read More »Turning Open Knowledge Into Public-Interest AI-Infrastructure: The Wikidata Embedding Project















