Medical Knowledge Graphs in Healthcare

Updated 23 November 2025

Medical knowledge graphs are structured, semantic representations that integrate diverse biomedical data such as genes, diseases, drugs, and clinical records.
They employ modular ETL pipelines and standardized ontologies (e.g., MONDO, HPO) to ensure interoperability, reproducibility, and precise semantic mapping.
Advanced methods like LLM-driven extraction and graph neural networks enhance predictive modeling and clinical decision support through improved data integration.

A medical knowledge graph (KG) is a structured, semantic representation that encodes biomedical concepts, entities, and their relationships, supporting integration, inference, and downstream analytics in healthcare and life sciences. Medical KGs formalize heterogeneous data—spanning genes, diseases, drugs, symptoms, procedures, practitioner profiles, patient records, regulations, and more—into interconnected graphs designed for search, reasoning, explanation, and predictive modeling at biomedical scale.

1. Construction Methodologies and Standards

Medical KG construction requires the integration of diverse data sources and adherence to standard schemas for semantic and operational interoperability. Major platforms employ modular extract–transform–load (ETL) pipelines, typified by KG-Hub (Caufield et al., 2023), which standardizes workflow into sequential download, transformation, and merge operations:

Download: Raw sources (relational tables, JSON, OWL ontologies, text) are retrieved and cached, ensuring reproducibility and fault tolerance.
Transform: Each source is mapped into a subgraph representation via scripted rules (e.g., Koza in KG-Hub), mapping native schemas into a canonical node and edge model (typically Biolink Model).
Merge: Subgraphs are integrated using tools such as KGX, which canonicalizes identifiers, unifies categories (e.g., biolink:Gene, biolink:Disease), and normalizes edge types.

Integration of formal biomedical ontologies—MONDO (diseases), HPO (phenotypes), GO (functions), CHEBI (chemicals)—provides an explicit concept hierarchy and supports ontological reasoning by encoding subclass and part_of relations as graph edges. Downstream, the resulting graphs are versioned, annotated with provenance at the source, transformation, and build levels, and deposited at stable URLs for access and reuse (Caufield et al., 2023). These practices are central to reproducibility, comparability, and trust in medical KGs.

Clinical KGs entering healthcare applications must also adhere to regulatory, privacy-preserving, and localization standards, as exemplified by medicX-KG which merges country-specific registries, clinical formularies, and pharmacological databases into a semantically unified, provenance-rich graph for point-of-care decision support (Farrugia et al., 22 Jun 2025).

2. Automated Extraction and Enrichment from Unstructured Data

Recent advances enable programmatic construction of medical KGs from semi- or unstructured data (clinical text, EMRs, scientific literature) using information extraction (IE) pipelines. LLM-driven approaches parse raw text for entities and relations, utilizing structured prompt templates and expert-curated instruction sets to improve extraction quality and minimize hallucination (Arsenyan et al., 2023, Sengupta et al., 30 Sep 2025):

Entity/Relation Extraction: Named Entity Recognition (NER) models (often transformer-based, e.g., BioBERT, SciBERT, ClinicalBERT) identify spans such as diseases, treatments, and risk factors. Relation extraction models identify directed links (e.g., disease–treated_by–drug), returning triples for KG insertion.
Prompt Engineering: In LLM-based systems (e.g., Medaka, GraphCare), detailed, schema-constrained prompts elicit structured output, and majority voting across sampled outputs assigns per-triple confidence scores, with only high-confidence relations retained in the final KG (Sengupta et al., 30 Sep 2025, Jiang et al., 2023).
Semantic Enrichment: Automated pipelines use external ontology services (BioPortal) to enrich concept nodes with synonyms, definitions, and hierarchy, and employ ClinicalBERT or similar embeddings to infer missing semantic links—either between clusters of enriched documents or directly between node pairs—thus increasing connectivity and completeness of the KG (Khalid et al., 21 Apr 2024).

Data cleansing, deduplication, and manual or semi-automatic validation remain essential, particularly in domains with complex synonymy or regulatory heterogeneity. Coverage, precision, recall, and human/LLM-audited evaluation frameworks are used to quantify extraction accuracy, with precision rates surpassing 96% in tightly constrained pipelines (e.g., Medaka) (Sengupta et al., 30 Sep 2025).

3. Modeling, Semantic Reasoning, and Completion

Medical KGs support symbolic reasoning, path-based inference, and representation learning through a combination of rule-based and distributional methods:

Ontological Inference: Use of OWL/RDF ontologies (e.g., OncoNet Ontology for cancer biomarkers) enables deductive closure and logical rule mining (e.g., Biomarker ⊑ ∃causes.Cancer), supporting federated SPARQL/DLx queries and explainable ML (Karim et al., 2023).
Path-Based Reasoning: Approaches that extract chain-based reasoning paths (e.g., PRA, path-RNN) are extended by incorporating BERT-encoded textual semantics of entities and relation paths. Textual embedding of both graph symbols and multi-hop chains mitigates the long-tail sparsity problem—improving completion accuracy (MAP) by 3–5% and enabling path-level interpretability (Lan et al., 2021).
KG Embeddings and Graph Neural Networks: Translational (TransE, TransH) and probabilistic/demographic-aware (DARLING) KG embedding models provide vectorized representations for link prediction, enabling personalized recommendations and risk assessment in EMR-derived KGs. Demographic projections into subspaces (via hyperplanes) allow modeling of age/gender/ethnicity-specific medical facts (Guluzade et al., 2021).
Hypergraph and Personalized KG Modeling: For precision medicine, frameworks like HypKG and GraphCare contextualize generic biomedical KGs with patient EHRs using hypergraph transformers or bi-attention GNNs, respectively, resulting in substantial AUROC/F1-score improvements in downstream tasks (e.g., mortality, readmission) (Xie et al., 26 Jul 2025, Jiang et al., 2023).

4. Applications in Healthcare, Research, and Clinical Decision Support

Medical KGs underpin a diverse array of healthcare applications and research use cases:

Clinical Prediction and Risk Modeling: Personalized and contextualized KGs integrated with GNN architectures (e.g., GraphCare, HypKG) enable learning of robust, compact patient representations, leading to superior predictive power in readmission, length-of-stay, and drug recommendation tasks—often with notable gains (AUROC up to +8.8%) over non-KG baselines (Jiang et al., 2023, Xie et al., 26 Jul 2025).
Search, Retrieval, and Patient/Provider Matching: Rich ontology-driven schemas enable complex graph-structured queries for provider discovery (e.g., find pediatrician open on weekends near a given location), achieving significantly higher coverage over traditional IR baselines (hybrid KG+IR: 70% vs. 30% for Lucene alone) (Kejriwal et al., 2023).
Drug Safety, Repurposing, and Biomarker Discovery: Medically focused KGs—supporting structured SPARQL and subgraph exploration—facilitate DDI/ADR monitoring, hypothesis-driven drug repurposing (e.g., literature-derived causal inference for COVID-19 therapeutics), and explainable biomarker identification for oncology (Karim et al., 2023, Zhang et al., 17 Aug 2025).
Fact-Checking and Explainability in Medical LLMs: KGs serve as referenceable ground-truths for automated evaluation of LLM-generated responses (FAITH), achieving substantially higher correlation with clinician judgments (Pearson’s ρ=0.696) and superior robustness to paraphrasing compared to textual overlap metrics. Path-based KG evidence provides both high-fidelity verification and fault localization (Zhou et al., 16 Nov 2025).

5. Evaluation, Transparency, and Community Standards

Assessment and widespread adoption of medical KGs depend on adherence to rigorous evaluation principles and community-driven standards:

FAIR Principles and Provenance: Modern KGs are judged against binary compliance matrices emphasizing access, provenance, schema documentation, versioning, evaluation, and licensing—using frameworks inspired by FAIR, OBO Foundry, and Biolink Model (Cortes et al., 29 Aug 2025). Best-in-class KGs (e.g., RTX-KG2, Monarch) provide downloadable full graphs, public APIs, per-node/edge provenance, changelogs, and clear licensing. Median compliance across 16 surveyed KGs is ≈70%, with significant room for maturation.
Schema and Terminology Harmonization: Adoption of Biolink Model and KGX exchange format is recommended for cross-KG interoperability, consistent node/edge typing, and automation of metadata provenance extraction.
Versioning and Automated Integration: Continuous Integration (CI) pipelines, public issue tracking, and machine-readable KG overview files enable transparent updates, external review, and reproducibility.
Transparency-Accuracy Trade-offs: Node-based embedding methods (M-KGA) yield higher accuracy and explainability in graph completion, at increased computational cost, compared to faster but more opaque cluster-based approaches (Khalid et al., 21 Apr 2024).

6. Advanced Reasoning, Retrieval Augmentation, and Future Directions

Cutting-edge research leverages integrated KG–LLM frameworks for enhanced medical reasoning and retrieval-augmented generation (RAG):

Hypothesis-Driven KG Exploration: LLMs generate hypothesis-rich anchor entities, which enable dense inference-chain selection and fine-grained reranking of KG evidence. Hybrid RAG systems (HyKGE, medIKAL) demonstrate measurable increases in medical QA accuracy (EM gain up to +28%), faithfulness, and explainability, with minimal LLM interactions (Jiang et al., 2023, 2406.14326).
Temporal and Continual KG Evolution: New platforms (MedKGent) incrementally construct, reinforce, and update KGs using self-consistent LLM agents over the biomedical literature time series. This supports temporal hypothesis tracking, uncertainty-aware link weighting, and dynamic, minute-granularity updates (Zhang et al., 17 Aug 2025).
Explainability and Clinician Trust: Path- and subgraph-level explanations—supported by interpretable attention, explicit path ranking, and provenance-traceable query results—address regulatory and clinician-auditability requirements, enabling KGs to serve as both prediction engines and rationalization tools (Garg et al., 2023).

7. Challenges and Research Frontiers

Despite substantial progress, medical KGs must overcome major challenges:

Data Heterogeneity and Sparsity: Long-tailed distributions of entities/relations necessitate embedding and path-based models that utilize semantic information to generalize from sparse observations.
Scalability and Automation: Efficient, accurate construction for multi-million node graphs requires scalable ETL, KG embedding, and IE pipelines.
Semantic Alignment and Local Adaptation: Integrating jurisdiction-specific regulatory data, patient-level EHRs, and external knowledge (molecular, clinical, regulatory) remains a technical hurdle, as does supporting multi-modal extensions (e.g., images, lab graphs).
Coverage and Maintenance: Real-time update pipelines, complete and explicit schema migration, and community-maintained standards are essential for maximizing translational impact and clinical trustworthiness.

Medical knowledge graphs provide the foundational infrastructure for integrative, explainable, and actionable AI in clinical and biomedical domains, unifying heterogeneous data into semantic graphs optimized for search, reasoning, and robust machine learning (Caufield et al., 2023, Kejriwal et al., 2023, Lan et al., 2021, Khalid et al., 21 Apr 2024, Sengupta et al., 30 Sep 2025, Theodoropoulos et al., 2023, Zhou et al., 16 Nov 2025, Arsenyan et al., 2023, Rosenbaum et al., 14 Dec 2024, Karim et al., 2023, Guluzade et al., 2021, Zhang et al., 17 Aug 2025, Cortes et al., 29 Aug 2025, Jiang et al., 2023, Xie et al., 26 Jul 2025, Jiang et al., 2023, Garg et al., 2023, Farrugia et al., 22 Jun 2025, 2406.14326).