Entity resolution (ER) is a complex process focused on data quality for knowledge graph construction and updates, with crucial impact on the quality and trust of downstream AI apps.

Paco Nathan shows how to use ER with open data to construct a KG in Neo4j, then used in GraphRAG based on LlamaIndex.

We’ll focus on linking multiple datasets (beneficial ownership, sanctions, GLEIF, etc.) regarding corporates in the London metro area, then explore hidden relations through graph visualization and chat interaction.

This example illustrates KG work used in production to investigate _ultimate beneficial owner_ (UBO) and sanctions compliance.

Get certified with GraphAcademy: https://dev.neo4j.com/learngraph
Neo4j AuraDB https://dev.neo4j.com/auradb
Knowledge Graph Builder https://dev.neo4j.com/KGBuilder
Neo4j GenAI https://dev.neo4j.com/graphrag