A Unified Graph Approach for the Next Era of Intelligent Systems 

Written by Soumen Chakraborty, Vice President, Artificial Intelligence

During a recent Life Sciences data modernization workshop, a VP paused and asked: 

“Should we use RDF or a Property Graph? Which one works better for AI and agentic workflows?” 

We hear this question all the time. 

But the real issue is not choosing one over the other — it’s the assumption that they serve the same purpose. 

• RDF and LPG solve fundamentally different problems
• AI behaves differently with each
• No production-grade AI system uses either in isolation

After delivering ontology-driven extraction, lineage systems, record-level DQ agents, and protocol-mining pipelines, one truth is clear: 

Modern enterprise AI requires a hybrid of RDF, LPG, ontologies/schemas, and vector grounding. 

This blog clarifies where each fits — with real examples, not theory.

1. The Two Graph Families in Practice

RDF (Resource Description Framework) 

RDF is the semantic layer — designed for meaning, rules, and governance. It represents knowledge as triples: 

(Study123 — hasTrialPhase — Phase3) 

RDF excels when enterprises need: 

• • Standard terminology 
  • Regulatory clarity 
  • Precise definitions 
  • Machine-understandable meaning 
  • Validation via SHACL 
  • Explainability 

Where Fresh Gravity Used RDF: Protocol Mining & Document Digitizer 

For a Life Sciences client, our ontology (RDF + OWL) is defined: 

• • What a Protocol, Arm, Cohort, Endpoint, and Procedure actually mean 

• • How do the Study Design elements relate 

• • Which metadata fields are mandatory 

• • How extracted entities must be validated 

RDF did not extract text on its own — schemas, and AI did that. But RDF ensured the extracted entities were: 

• • Consistent 
  • Valid 
  • Semantically aligned 
  • Explainable 

RDF gave us the meaning. AI + schema gave us close to 100% accurate extraction. 

LPG (Labelled Property Graph) 

LPG is the operational layer — optimized for: 

• • High-speed traversal 
  • Relationship intelligence 
  • Pattern discovery 
  • Graph analytics 
  • Fraud-like behavior
  • Identity linking 

Nodes and edges can carry properties: 

(Patient456 {age:72}) —[filedClaim]-> (Claim9823) 

Where Fresh Gravity Used LPG: Identity Resolution & Record-Level Lineage 

Identity Resolution 

For a data stewardship program, LPG enabled us to: 

• • Detect clusters of duplicate patients 
  • Traverse millions of edges 
  • Identify hidden connections in seconds 

Record-Level Identity Lineage 

For another client, LPG allowed us to: 

• • Trace every “data hop” or transformation 
  • Explore lineage metadata across dozens of pipelines 
  • Uncover indirect dependencies 
  • Pinpoint the exact upstream sources affecting a record 

This level of traversal is complicated with RDF, but LPG handled it effortlessly. 

2. Important Reality Check: Graphs Alone Don’t Stop Hallucination or Improve Extraction

Some AI blogs (- even GPT models) claim: 

“Use an ontology/graph and hallucinations disappear.” 

That is not true in practice (at least for the current generation of models as of this writing). 

Across our implementations, three facts consistently hold: 

Fact 1 — Schema-driven extraction is more reliable than ‘only’ ontology/graph-driven extraction 

LLMs respond far better to: 

• • Structured JSON schemas 
  • Output templates 
  • Examples 
  • Field definitions 

Ontologies enrich extraction but rarely improve raw extraction accuracy by themselves. 

Document Digitizer uses: 

• • Schemas → for extraction 

• • RDF/OWL → for classification, consistency, validation 

Fact 2 — Vector databases (VDB) are essential for grounding 

LLMs do not efficiently query ontologies or graphs directly. 

We rely on: 

• • Vector embeddings 
  • Hybrid retrieval 
  • Chunk-level semantic search 
  • Ontology-backed disambiguation 

VDB ≠ RDF ≠ LPG — each solves a different grounding need. 

Fact 3 — AI needs a multi-layered approach 

A mature enterprise AI stack uses: 

1. • Schemas → structure 
   • Ontologies (RDF/OWL) → meaning 
   • LPG → relationship intelligence 
   • Vector DB → grounding 
   • Agents & Tools (with LLMs/SLMs) → orchestration 

Graphs are powerful — but only part of the system. 

3. When Should You Use What? 

Use RDF when you need: 

• • Semantic consistency 
  • Domain meaning 
  • Controlled vocabularies 
  • Lineage semantics 
  • Rule validation (SHACL) 
  • Regulatory auditability 

Example:
Validating Clinical Trial metadata. 

Use LPG when you need: 

• • Fast traversal 
  • Relationship patterns 
  • Fraud-like detection 
  • Dynamic 360 views 
  • Deduplication & linking 
  • Impact exploration at scale 

Example:
Detecting duplicate patient entities across millions of rows. 

Use BOTH when you need: 

• • AI-ready hybrid Knowledge Base, semantic + operational lineage
    Example:
    RDF (via OWL) defined: 

“A Form must belong to one Submission.” 

“A Submission must reference ≥ 1 Report.” 

LPG answered real questions: 

“If variable X changes, which forms break?” 

“Which downstream submissions are impacted?” 

“Which datasets feed those forms?” 

• • End-to-end explainability 
  • Scalable data quality and data governance workflows 
  • Mapping + rule enforcement + traversal 

Which, honestly… is what every enterprise wants today. 

4. The Future: Agentic AI Needs Meaning + Relationships + Retrieval

AI agents operating on enterprise data need to: 

• • Enforce semantic rules (RDF + SHACL) 
  • Understand domain meaning (OWL) 
  • Analyze relationships efficiently (LPG) 
  • Detect anomalies & duplicates (LPG) 
  • Retrieve grounding context (Vector DB) 
  • Maintain traceability (RDF + lineage) 

Every successful FG delivery has followed this pattern: 

• • RDF for meaning 
  • LPG for relationship intelligence 
  • Schemas for extraction 
  • Vectors for grounding 
  • AI/LLMs for reasoning 
  • Agents for automation 

This is what modern enterprise AI actually looks like. 

Final Thoughts 

If your team is debating: 

“RDF vs LPG — which one should we choose?” 

You’re asking the wrong question. 

The real question is: 

“Where do we need semantic meaning and constraint-based reasoning?
Where do we need fast traversal and operational insight?
And how will our AI agents orchestrate schemas, vectors, RDF, and LPG together?” 

At Fresh Gravity, we stopped guessing.
We built. We delivered. What consistently works is a hybrid, purpose-driven architecture for enterprise AI.