MKG Series · Part 02 of 03

Methodology —
Building the Graph.

The biggest mistake is building a graph before defining its purpose. Nine phases take a medical knowledge graph from business intent to a live, AI-connected asset — in that order.

9Phases to AI Integration
1Mandatory Medical Review Gate
4Common Graph Database Technologies

Purpose Before Architecture

The first four phases determine whether the graph will solve a real business problem or become an expensive, ownerless data project.

01
Phase 1
Define the Business Use Case
The biggest mistake: building a graph before defining its purpose. Medical Affairs → MSL Copilot · Medical Information → Faster response generation · Marketing → Evidence recommendation engine · Scientific Exchange → AI-powered HCP portal.
02
Phase 2
Design the Ontology
The ontology defines what entities exist and what relationships exist: Disease, Drug, Biomarker, Trial, Publication, Guideline, Patient Population · treats, targets, evaluates, recommends, reports, associated_with, contraindicated_for.
03
Phase 3
Acquire Source Data
Internal: Medical Information database, approved claims, Core Data Sheets, study reports. External: PubMed, ClinicalTrials.gov, NCCN, ESMO, FDA, EMA.
04
Phase 4
Extract Entities
NLP/LLM extraction turns prose into structured nodes. Text: “Nivolumab improved overall survival in PD-L1 positive NSCLC.” → Drug=Nivolumab, Outcome=Overall Survival, Biomarker=PD-L1, Disease=NSCLC.

From Raw Extraction to a Validated, AI-Connected Graph

The second half of the methodology is where quality is won or lost — entity resolution and validation are not optional steps to skip under time pressure.

05
Phase 5
Extract Relationships
Nivolumab → improves → Overall Survival in PD-L1 Positive NSCLC. Done via NLP pipelines, LLM extraction, and human curation — usually a combination of all three.
06
Phase 6
Entity Resolution
Critical step. “NSCLC,” “Non-Small Cell Lung Cancer,” and “Non-small-cell lung carcinoma” all become a single canonical entity.
07
Phase 7
Populate the Graph
Common technologies: Neo4j (most common in pharma), Amazon Neptune (enterprise scale), Stardog (semantic healthcare), GraphDB (ontology-heavy implementations).
08
Phase 8
Validation
Medical review is mandatory. Check for hallucinated relationships, outdated evidence, duplicate entities, and incorrect mappings.
09
Phase 9
Connect to AI
LLM → RAG Layer → Knowledge Graph → Evidence Sources. This is where most pharma companies are heading — the graph becomes the grounding mechanism.
LLM → RAG Layer → Knowledge Graph → Evidence Sources. The graph becomes the grounding mechanism.

What Does This Actually Unlock?

Explore Part 03 — the pharma-specific future state, from Evidence Graphs to Scientific Exchange Knowledge Graphs that create measurable business value.

From Graph to Business Value → Back to Foundations