Entity Resolution (ER) serves to interlink fragmented and dispersed data, facilitating the identification of records representing identical real-world entities. This function is pivotal for intelligence analysis, enriching investigations by ensuring comprehensive and uniform data merging.
This session will showcase a robust end-to-end approach for precise and effective data consolidation, adaptable to batch or incremental processing and predominantly reliant on graphs. Key themes will encompass customizable similarity rules, harnessing node attributes and relationship patterns, and strategic utilization of Neo4j indexes and GDS. Additionally, we’ll delve into diverse data modeling strategies, evaluating their advantages and drawbacks and how to accommodate dynamic data changes.

With Federica Ventruto

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