Knowledge Graphs in Health

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The use of graph databases, semantic technologies, and knowledge graphs for health data integration, clinical decision support, and longitudinal patient records.

Overview

Knowledge graphs are increasingly recognized as a foundational technology for healthcare AI. Neo4j's research shows "healthcare leads in knowledge graphs" and CIOs "can't afford to scale AI without a knowledge graph foundation."[1] Towards Data Science notes that healthcare leads other industries in knowledge graph adoption.[2]

Key Projects

AIDAVA (EU)

AIDAVA — €7.7M Horizon Europe project (2022–2026) creating a virtual assistant for automated curation and publishing of personal health data. Uses Personal Health Knowledge Graphs (PHKG) based on SNOMED, FHIR, and LOINC ontologies. 13 partners from 9 countries. Coordinator: Maastricht University. Knowledge graph technology by Ontotext.[3]

AllegroGraph (Franz Inc.)

Franz Inc.'s AllegroGraph is a semantic graph database widely used in life sciences and healthcare. Supports RDF, SPARQL, and knowledge graph applications for clinical data integration and linked health data.[4]

Neo4j Health Applications

Neo4j, the leading graph database, has published extensively on healthcare knowledge graphs including MedQGraph (Temporal Medical Knowledge Graphs for AI-Driven Healthcare Insights) and clinical data integration patterns.[5]

GraphDB (Ontotext)

GraphDB by Ontotext (now part of Graphwise) is an enterprise knowledge graph database used in health data integration. Powers the knowledge graph backbone of AIDAVA.[6]

OHDSI / OMOP Common Data Model

The OHDSI (Observational Health Data Sciences and Informatics) community uses the OMOP Common Data Model with graph-based approaches for observational health research. Recent work includes graph visualization and validation of drug mappings using LLMs.[7]

PubMed Knowledge Graph 2.0

Connects papers, patents, and clinical trials in biomedical science using graph structures.[8]

i-HD Projects

i-HD participates in multiple projects using knowledge graph approaches for health data, including IDERHA (integration of heterogeneous data), QUANTUM (data quality labels), and SYNTHEMA (synthetic data generation).[9]

LLM + Knowledge Graphs

Large language models are increasingly used to construct and query health knowledge graphs. Nature published on "Large language model powered knowledge graph construction for mental health exploration."[10] Frontiers published on "Unlocking electronic health records: a hybrid graph RAG approach to safe clinical AI."[11]

Technology Stack

Common technologies used in health knowledge graphs:

  • Graph Databases: Neo4j, GraphDB (Ontotext), AllegroGraph (Franz Inc.), Amazon Neptune
  • Standards: RDF, OWL, SPARQL, SHACL
  • Health Ontologies: SNOMED CT, LOINC, HL7 FHIR, ICD-10, OMOP CDM
  • Integration: ETL pipelines, FHIR APIs, NLP extractors
  • AI/ML: Graph neural networks, LLMs for KG construction, embedding models

See Also

  1. ↑ Neo4j (2025): "Why Healthcare CIOs Can't Afford to Scale AI Without a Knowledge Graph Foundation" — neo4j.com
  2. ↑ Towards Data Science: "Why Healthcare Leads in Knowledge Graphs"
  3. ↑ aidava.eu / OpenAIRE grant 101057062
  4. ↑ Franz Inc. — franz.com/agraph
  5. ↑ Neo4j — neo4j.com, MedQGraph
  6. ↑ PR Newswire (2024): "Semantic Web Company and Ontotext Merge to Create Knowledge Graph and AI Powerhouse Graphwise"
  7. ↑ medRxiv (2025): "Rx Norm for Europe — Toward the representation of medicinal products in the OMOP CDM"
  8. ↑ Nature (2025): "PubMed knowledge graph 2.0"
  9. ↑ i-hd.eu/projects
  10. ↑ Nature (2025)
  11. ↑ Frontiers (2025)
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