Technologies and Innovations 1.3


Advanced technical presentations, delving into leading software, integrations, and techniques to share the latest leading edge developments and solutions.

Wednesday, April 22, 2020 - 10:30 to 12:15


When Time Meets Semantics: Extending RDF for Immutability

In this talk, Brian proposes we treat time as primary componentry to a semantic implementation, alongside data. Extending RDF to include timestamps allows data to exist in a temporal context for better SPARQL consumption, and allows machines (AI/ML) to ingest higher quality data for better decisions. Brian will speak about the benefits of Time Travel (query a state of a graph at a specific point in time) and time-joins (the ability to "join" two states of a graph for deeper time-oriented analysis.)

Explainable AI: Finding Correlation and Causation in Knowledge Graphs

Franz is involved in a number of Knowledge Graphs that capture real world events. In each domain we work to understand the correlation and co-occurrence of certain events so, given an event X, we can predict the chance of event Y. We all were taught that correlation does not equal causation but following Judea Pearl’s famous book "The Book of Why" we developed several techniques that add the arrow of time to our data analysis so we can start talking about 'causal' relationships in real world Knowledge Graphs.