Wikidata adds AI vectors to graph and knowledge bases. Here's why

An essential component for building more trustworthy AI lies in creating a more solid data foundation on top of graph databases, knowledge graphs, and vector databases. Each element plays a fundamental role in representing the relationships between things, searching through these relationships, and presenting an appropriate subset to AI tools.

Now, the Wikimedia Foundation is adding a vector database to improve search and discovery on Wikidata, a sister platform to Wikipedia. I recently spoke with Jonathan Fraine, Head of Engineering and Philippe Saadé, AI/ML Project Manager at Wikimedia Deutschland, about the interplay between knowledge graphs, graph databases, and vector databases for the future of the semantic web.

Wikidata is widely used under the hood of search engines and other tools for creating knowledge cards and different use cases. Traditional keyword-based search methods couldn’t adequately interpret the context or relationships between entities. Additionally, as the dataset expanded, Wikimedia needed ...