In Gartner’s 2024 Artificial Intelligence Hype Cycle, there are two big movers. The most mature of the two is the topic of this session: "Knowledge Graphs" which, according to Gartner, just reached the "Slope of Enlightenment" ("AI Engineering" - now reaching the "Peak of Inflated Expectations" - is the second).

But there is more to "Knowledge Graphs" than their relevance to the current hype darling, “AI.”

Using W3C standards, Knowledge Graphs model all kinds of complex sets of entities and their relationships in an intuitive way. Compared to “plain" graphs, they offer flexible schema languages, machine-readable semantics and the direct use of existing open schemas and ontologies.

In this session, you will first get a (short) refresher on graphs and then learn about:

- The difference between property graphs and knowledge graphs.

- RDF: The base of the W3C Knowledge Graph standard.

- SPARQL.: The knowledge graph "SQL".

- OWL: The open world assumption and graph-based reasoning.

- SHACL: The closed world assumption and constraining.

- Some of the key players implementing Knowledge Graph databases.

- How to start by yourself.

Christoph Seck

Christoph is a Global Data Architect at Kühne + Nagel. Previously, he worked as a BI and data architect for various system integrators, implementing data platform projects mostly within the Microsoft ecosystem, was a long time Microsoft P-TSP and Visiting Lecturer at the University of Hildesheim.

He is co-founder and chapter lead of the PASS Group Hannover/Göttingen and a regular speaker at PASS groups and conferences, covering topics such as data strategy, databases, agile methodologies, and blockchain.