Knowledge Graphs & Ontologies
The foundation without which GenAI does not work in the enterprise. A free live session in English — technically concrete, with a live walkthrough from business question to explainable answer.
Register for the webinar
Free of charge · 22 July, 1:00 pm (CEST) · online.
Why a Knowledge Layer is the prerequisite for reliable AI
Copilots, agents, RAG systems: the demos convince, but the measurable value fails to materialise. The reason is not the model — without formally modelled enterprise knowledge, LLMs do not deliver reliable answers.
Larger context windows, more MCP servers and the next GPT release will not solve this. It is not about readability — text is already readable. It is about interpretability: classes, relationships, constraints and rules from which a machine can reason logically.
The established answer is knowledge graphs and ontologies — 25 years of research, open standards (RDF, OWL, SPARQL, SHACL), suddenly relevant again. Google, Facebook, Siemens, Bayer and entire further industries build on exactly this foundation.
This webinar makes the topic concrete
- 01
Why LLMs hallucinate without a knowledge graph
And why more data, larger context windows and more MCP servers will not solve the problem.
- 02
Knowledge Graph vs. Ontology vs. RAG
A clear distinction between the terms — and when which tool holds up.
- 03
Anatomy of an ontology
Classes, relationships, constraints, reasoning — what a machine can logically infer from.
- 04
How a knowledge graph works on a technical level
RDF/OWL, triple stores, SPARQL, federation — and why proprietary semantic layers become a lock-in risk.
- 05
How agents make use of a knowledge graph
Integration, semantic retrieval, explainability — with a live walkthrough from business question to explainable agent answer, including an ontology excerpt and SPARQL.
- 06
How to get started step by step
No large-scale programme, with a realistic pilot scope — plus Q&A.
Who this webinar is designed for
The session is technically concrete but requires no prior Semantic Web knowledge.
CIO
Reliability and provenance of AI answers as an architecture question — not a bet on models.
Chief Data / AI Officer
From a portfolio of pilots to measurable value: the foundation that makes RAG projects viable.
Enterprise Architect
Open standards (RDF, OWL, SPARQL) instead of a proprietary layer — assessing lock-in risks.
AI leaders
For everyone who needs to deliver more than the next pilot in the coming 12 months.
Register for the webinar
Free of charge · 22 July, 1:00 pm (CEST) · online.
What you will take away
- A clear distinction: knowledge graph, ontology, RAG.
- The technical foundation: RDF/OWL, SPARQL, triple stores — without lock-in.
- A live walkthrough from business question to explainable agent answer.
- An entry path without a large-scale programme, with a realistic pilot scope.