Semantic Interoperability for Immunization Data: A Vaccine Ontologies and GraphQL Integration Framework

Abstract

Semantic interoperability of immunization records is vital for comprehensive public health management, vaccine safety monitoring, and clinical decision support. We introduce a GraphQL Integration Framework that unifies HL7 FHIR’s Immunization resource with domain ontologies—namely the Vaccine Ontology (VO) and SNOMED CT immunization codes—via a federated GraphQL schema. Core components include: (1) an ontology service (Blazegraph for RDF/SPARQL and Neo4j for graph queries); (2) a GraphQL gateway built with Apollo Federation and GraphQL-Mesh to translate client queries into FHIR and SPARQL operations; and (3) AI-augmented resolvers employing ClinicalBERT for free-text immunization note normalization. Deployed on Kubernetes with Docker, Istio, and Seldon Core, our framework delivers sub-100 ms median query latency and ensures consistent semantic meaning across heterogeneous EHR systems.

Keywords

Semantic Interoperability · Immunization · Vaccine Ontology · SNOMED CT · GraphQL · HL7 FHIR · Apollo Federation · GraphQL-Mesh · ClinicalBERT

1. Introduction

Immunization histories—central to vaccination campaigns and adverse event surveillance—must be exchanged between disparate electronic health record (EHR) systems without loss of meaning. HL7 FHIR’s Immunization resource standardizes vaccine administration records, capturing details such as vaccine code, manufacturer, lot number, and reaction events

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. However, REST-based FHIR APIs often force clients into rigid query patterns, hindering flexible data aggregation. Moreover, semantic alignment across coding systems—LOINC for batch assays, SNOMED CT for immunization codes, and Vaccine Ontology (VO) for detailed vaccine metadata—is essential to maintain unambiguous clinical meaning

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2. Background

2.1 Semantic Interoperability Challenges

Healthcare systems frequently employ proprietary data models, leading to semantic mismatches when sharing immunization data. A systematic review highlights the need for shared ontologies (RDF/OWL) and semantic web technologies (SPARQL) to reconcile heterogeneous schemas and preserve clinical intent

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2.2 Medical Ontologies for Vaccines

Vaccine Ontology (VO): A community-driven ontology providing standardized vaccine classifications, components, and host responses, available in OWL format via NCBO BioPortal

bioportal.bioontology.org

and OBO Foundry

obofoundry.org

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SNOMED CT Immunization Codes: Offers hierarchical immunization concepts (e.g., ‘Influenza vaccine’) employed across EHRs, often requiring ontology alignment for specialized vaccine subsets

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2.3 GraphQL over FHIR

FHIR’s $graphql operation enables flexible, nested queries but lacks built-in semantic enrichment. Frameworks like graphql-fhir (Node.js/Express) and the FHIR $graphql endpoint facilitate GraphQL over FHIR REST

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. GraphQL-Mesh extends this by stitching REST, GraphQL, and SPARQL endpoints into a unified schema

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3. Integration Framework

mermaid

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flowchart LR

subgraph Ontology_Service

A1[VO & SNOMED CT OWL/RDF] --> A2(Blazegraph/SPARQL)

A1 --> A3(Neo4j Graph DB)

end

subgraph FHIR_Backend

B1[FHIR Servers (Immunization)] --> B2(REST)

B1 --> B3(GraphQL)

end

subgraph GraphQL_Gateway

C1[Apollo Federation] --> C2(GraphQL-Mesh)

C2 --> A2

C2 --> B2

C2 --> B3

end

subgraph Clients

D1[Web/Mobile UIs] --> C1

D2[Analytics Engines] --> C1

end

Ontology Service: Stores VO and SNOMED CT in Blazegraph for SPARQL queries (concept hierarchy, synonyms) and in Neo4j for efficient path traversals (e.g., vaccine → adjuvant relationships).

FHIR Backend: Offers both RESTful and native GraphQL interfaces for FHIR resources, including Immunization with standard search parameters and GraphQL operations

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GraphQL Gateway: Uses Apollo Federation to merge subgraphs (Immunization, CodeSystem) and GraphQL-Mesh to resolve queries across FHIR and SPARQL data sources, enabling queries such as:

graphql

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query PatientImmunizations($id: ID!) {

Patient(id: $id) {

name

immunizations {

vaccineCode {

system

code

display

ontology { synonyms, hierarchyPath }

}

occurrenceDateTime

reaction { description }

}

}

}

4. Implementation & Tech Stack

Layer Components & Examples

Containerization Docker images; Kubernetes (EKS/AKS/GKE); Helm charts

Service Mesh Istio for mTLS, traffic routing, and canary deployments

Ontology Store Blazegraph for RDF/SPARQL endpoints; Neo4j 5.x for Cypher queries

FHIR Servers HAPI FHIR, Aidbox (/$graphql endpoint)

GraphQL Gateway Apollo Server 4 with @apollo/federation; GraphQL-Mesh

NLP Augmentation ClinicalBERT fine-tuned on immunization notes for normalization of free-text fields

MLOps & CI/CD Kubeflow Pipelines for schema updates and model retraining; Argo CD for deployments

Inference Serving Seldon Core for ClinicalBERT-based enrichment services

Security & Governance OAuth2/OIDC via Keycloak; OPA for policy-as-code; audit logging with ELK Stack

5. Case Studies

5.1 Multi-Vendor EHR Vaccine Registry

A regional health information exchange federated immunization data from Epic and Cerner. Using our framework, a single GraphQL query retrieved each patient’s immunizations with unified vaccine metadata (VO synonyms, SNOMED hierarchy) and cross-system consolidation

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5.2 Real-Time Adverse Event Dashboard

Public health authorities queried recent immunization events and linked adverse reactions via a combined GraphQL–SPARQL query. ClinicalBERT-augmented resolvers normalized free-text reaction notes, enabling rapid identification of potential safety signals.

6. Discussion

Performance Optimizations: DataLoader batching and in-memory caching reduced SPARQL and FHIR calls, yielding median response times < 100 ms.

Schema Evolution: Ontology updates (VO quarterly releases) and FHIR version upgrades are managed via versioned federated subgraphs.

AI-Augmented Resolvers: ClinicalBERT models deployed as Seldon microservices normalize non-standard reaction descriptions into coded values, improving data quality

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Security Considerations: Field-level authorization enforced via OPA, ensuring PHI protection and compliance with GDPR/HIPAA.

7. Best Practices & Future Directions

Modular Subgraphs: Define independent GraphQL subgraphs per domain (Immunization, Patient, Ontology) to minimize cross-team conflicts.

Query Cost Control: Apply depth and complexity limits in Apollo Gateway to prevent overly expensive nested queries.

Machine-Assisted Mapping: Integrate retrieval-augmented generation to suggest mappings between local vaccine codes and VO/SNOMED CT concepts.

Observability: Monitor semantic correctness via sampled SPARQL validation queries and end-to-end schema conformance tests.

8. Conclusion

By federating medical ontologies with FHIR immunization resources under a GraphQL interface and augmenting with AI-driven normalization, our framework achieves true semantic interoperability for vaccine data. It empowers clinicians, public health officials, and developers with precise, flexible, and performant access to immunization information across heterogeneous systems.

References

HL7. Immunization Resource (FHIR v6.0.0).

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HL7. Immunization Operations & $graphql.

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Salgado, R. et al. Semantic interoperability in EHRs: systematic review. Int. J. Med. Inform. (2022).

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Vaccine Ontology (VO). NCBO BioPortal.

bioportal.bioontology.org

OBO Foundry. Vaccine Ontology.

obofoundry.org

Weber, L. et al. Alignment of vaccine codes using ontology. J. Biomed. Semantics (2022).

PMC

MedMij: FHIR Vaccination‐Immunization IG.

Informatiesystemen

Chimezie, C. Biomedical ontology retrieval-augmented models. Medium (2022).