Dear Data Enthusiasts,
As part of the European Commission’s AI Innovation Month, join us at TUtheSky on the 29th of October to explore how knowledge graphs, robust orchestration, and rigorous fairness testing are shaping trustworthy AI.
A big thanks goes to our co-organizers from the AI Factory Austria and Neo4j for sponsoring the event.
Legit Check for AI - Grounding AI with Knowledge Graphs for Transparency and Trust at the Core
AI systems are only as trustworthy as the data they reason over. But when that data is highly interconnected — products, brands, trends, relationships — flat retrieval architectures like RAG fall short: they can't traverse context, can't explain their paths, and can't make their reasoning visible.
This talk shows how Knowledge Graphs change that equation.
Using a real-world dataset scraped from sources like Sneaker News, WWD, and StockX, we demonstrate how to build a Knowledge Graph from unstructured documents and use it to ground Large Language Models, making their outputs not just more accurate, but genuinely explainable.
We'll walk through:
Building the graph — extracting structured knowledge from unstructured web sources and modeling it as a connected graph
Grounding LLMs — how Knowledge Graph context replaces hallucination-prone retrieval and gives the model structured facts to reason over
Visualizing trust — how graph-based visualizations let users see why the AI answered the way it did, not just what it answered
In the sneaker world, a legit check separates what's real from what just looks right. AI deserves the same standard. Along the way, we'll make the case for why Knowledge Graphs outperform RAG and multi-tool agent architectures in domains where data relationships matter and show what trustworthy AI looks like when it can finally show its work.
Moritz Wegener, Data Scientist - ATVANTAGE GmbH
Moritz works on agentic AI, knowledge graphs, and moving AI systems from demo to real-world deployment — including graph-based fra