In this introduction training we will explore the basic concepts and building blocks behind knowledge graphs: nodes, edges, triples, IRIs. We will focus on RDF-based knowledge graphs and the surrounding W3C stack (RDF, RDFS, OWL, SHACL, SPARQL), while also contrasting them with labelled property graphs such as Neo4j so that attendees understand the trade-offs between the two paradigms.

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From there we will move into ontology modelling: how to design a schema that captures a domain, reuse existing vocabularies and ontologies, and validate data. We will review the architectures and stacks commonly deployed in KG based systems such as triplestores, federated SPARQL endpoints, ETL and mapping layers, and graph RAG systems.

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We will then dive into use cases from several domains and give an overview of how knowledge graphs are leveraged, together with the tooling that supports this work. Finally, we will look at how knowledge graphs and LLMs complement each other, and guide attendees on using LLMs for knowledge base design and population such as ontology drafting, entity and relation extraction, triple generation, and validation-in-the-loop. Lastly, we will explore how graphs can ground LLM outputs in return.

This workshop includes hands-on sessions to:

-Create and develop and model for entities and properties, in a toy example,

-Generate synthetic data and populate the graph

-Query the graph with SPARQL