Store data. Query knowledge.

Event Details

Talk 1:
Tommi Enenkel shows us how he went about using a knowledge graph as the data source for obtaining insights over complex Facebook data. He starts off by presenting the mechanism used to stream Facebook data and replicate it effectively into the knowledge graph.

Next, he takes us through an example of how this intelligent analytical tool performs in action. As he takes on the role of a journalist, he goes on to query the knowledge graph in an attempt to unfold the story of the infamous BVT, the Austrian intelligence agency, scandal.

Finally, he points out how choosing a knowledge graph as the database, enabled querying for knowledge as opposed to extracting data. He shows us how he could see over and beyond what’s obvious on the surface to provide valuable insights that surpass what Facebook offers as “metrics”.

Talk 2:
Soroush Saffari shares his experience in defining the schema for his first Grakn knowledge graph. He starts off by describing the dataset in the most human way possible and then shows us the transition of that initial description into a schema written in Graql, the language for the Grakn knowledge graph.

Next, he takes us through how a Grakn Client can be used to load actual data into the knowledge graph.

Finally, he demos how questions, typically asked by the client who owns the data, are formed into and executed as Graql queries to provide valuable insights over the dataset.