Knowledge Graphs for Locations in the Humanitarian and Development Sector

In the humanitarian sector, timely and accurate data is essential, yet master and reference data are often fragmented and siloed across systems as well as difficult to access or govern consistently. At the Norwegian Refugee Council (NRC), we are addressing this challenge by applying semantic web technologies and knowledge graph principles to master data management, with the aim of transforming how critical data is structured, related, and shared across the organization.

This session will be about our use case of modeling NRC’s organizational locations using a graph database. Locations—spanning country offices, field sites, and headquarters—were previously stored in disconnected systems with limited visibility into their hierarchical or operational relationships. By restructuring this data into a knowledge graph, we now capture the real-world relationships between locations, including physical containment, administrative hierarchies, attributes and programmatic linkages.

This graph-based model has significantly improved data accuracy, consistency, and accessibility across NRC. It has eliminated duplication, enabled dynamic queries across systems, and serves as a trusted single source of truth for both internal teams and integrated applications. Just as important, this model also supports GDPR compliance by providing clear data lineage and contextual traceability.

This successful implementation demonstrates how moving from static taxonomies to dynamic ontologies and knowledge graphs can unlock the true value of master data, empowering humanitarian decision-making with more intelligent, connected, and governable data ecosystems.

Speaker: Himanshu Ardawatia

Resources:
Get Started with Aura – https://bit.ly/3LOLrjh
Deployment Center – https://bit.ly/4jOelM3
Ground AI Systems and Agents with Neo4j – https://bit.ly/4oVsnyb

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