Session track: AI Engineering
Session time:
Session description:
Show how to build a complete GraphRAG pipeline that converts unstructured clinical notes into an explainable Neo4j knowledge graph using LLMs. Cover entity extraction, graph modelling, temporal relationships, Cypher queries, GraphRAG retrieval, and lessons learned. Demonstrate how graph-based reasoning provides transparent, evidence-backed answers and discuss how the same architecture can be applied beyond healthcare.
Speaker

Knowledge Graph Lead, Capgemini
Joakim has a background in Mathematics and is working as Knowledge Graph Lead for Capgemini. He has experience running Knowledge Graph project both from the public and private sector - in Sweden and abroad.