TL;DR
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Six ordered steps: what is a KG → RDF vs property graph → SPARQL/Cypher → ontologies → enterprise KG → GraphRAG.
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Budget 3–4 weeks part-time; includes modeling a small domain graph and running multi-hop queries.
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Pick one graph model early - RDF (standards, linked data) or property graphs (developer ergonomics). Most teams never need both.
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Knowledge graphs complement vector search - structure for relationships, embeddings for similarity. GraphRAG combines both.
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Depth is in linked guides - this path sequences them; each
/learn/*article goes deep.
Who This Path Is For
You should follow this roadmap if you:
- Need multi-hop queries ("suppliers of suppliers with compliance flags")
- Build GraphRAG or entity-aware search
- Integrate siloed data with shared identity and relationships
Prerequisites: Embeddings (for the GraphRAG finale). SQL and basic data modeling help.
Path at a Glance
GraphRAG builds a knowledge graph from source documents, clusters entities into communities, and retrieves graph-aware context for complex multi-hop questions.
| Step | Topic | Time | Outcome |
|---|---|---|---|
| 1 | Knowledge graph fundamentals | 2–3 hr | Model entities and edges |
| 2 | RDF vs property graphs | 2 hr | Choose your stack |
| 3 | Query languages | 3–4 hr | Run multi-hop queries |
| 4 | Ontologies & validation | 2–3 hr | Schema + constraints |
| 5 | Enterprise deployment | 3 hr | Identity, lineage, ops |
| 6 | GraphRAG | 3 hr | KG + LLM integration |
Total: ~15–18 hours + modeling project
Step 1: What Is a Knowledge Graph? (2–3 hours)
Read: Knowledge Graphs · Graph Databases
What you'll learn
- Nodes, edges, properties; when graphs beat SQL; KG vs document stores
Hands-on: Sketch five entities and eight edges; find one 3-hop question. Checkpoint: Explain why multi-hop paths are painful in SQL.
Step 2: RDF vs Property Graphs (2 hours)
Read: RDF · Property Graphs
What you'll learn
- Triples (subject–predicate–object) vs labeled nodes and relationships
- Standards landscape: W3C (RDF, OWL, SHACL) vs openCypher/GQL
- Store options: Neo4j, Neptune, GraphDB, Stardog, TigerGraph
Decision guide
| Choose RDF if… | Choose property graphs if… |
|---|---|
| Linked open data, academia, pharma | Product engineering, fraud, recommendations |
| SHACL validation, OWL reasoning | Native edge properties, Cypher ergonomics |
| Multi-vendor semantic web stack | Neo4j-first team and tooling |
Checkpoint
You documented RDF or LPG for your project - not "we'll decide later."
Step 3: SPARQL and Cypher (3–4 hours)
What you'll learn
- Pattern matching, variable-length hops, filters, parameterized queries
Hands-on: Implement your 3-hop question in SPARQL or Cypher. Checkpoint: Five queries in version control with expected outputs.
Step 4: Ontologies & Schema (2–3 hours)
Read: Ontologies · SHACL (RDF path)
What you'll learn
- TBox vs ABox; schema.org reuse; SHACL or LPG constraints on ingest
Hands-on: Ten classes, fifteen relationship types, three validation rules. Checkpoint: Loader rejects invalid data loudly.
Step 5: Enterprise Knowledge Graphs (3 hours)
Read: Enterprise Knowledge Graphs
What you'll learn
- Entity resolution, CDC ingest, governance, provenance on every edge
Hands-on: Map three source tables to canonical entities with provenance. Checkpoint: Architecture diagram from sources → graph → consumers.
Step 6: GraphRAG (3 hours)
Read: GraphRAG · Agentic RAG (optional)
What you'll learn
- Entity linking, community summaries, when GraphRAG beats vector-only RAG
Hands-on: Index 20 docs; answer one multi-hop question vector RAG misses. Checkpoint: Side-by-side vector vs GraphRAG on three relationship queries.
Capstone Project
Build a domain mini-graph end-to-end:
- Model 50–200 entities in your chosen format (RDF or LPG)
- Ingest from at least two synthetic or real sources with entity resolution
- Five SPARQL/Cypher queries powering a simple API or notebook
- Ontology validation on write
- One GraphRAG query demonstrating multi-hop advantage
Example domains: cybersecurity asset graph, movie/actor recommendations, supply chain, corporate org + project dependencies.
Common Sequencing Mistakes
| Mistake | Consequence | Prevention |
|---|---|---|
| Picking Neo4j then needing OWL reasoning | Costly rework | Step 2 decision matrix |
| Skipping ontologies | Schema drift, duplicate entities | Step 4 before bulk ingest |
| Graph everything | Expensive graph with no path queries | Step 1 - validate graph-shaped questions exist |
| GraphRAG without graph discipline | Fancy vector search with extra steps | Complete Steps 1–5 first |
Production Checklist
Before production graph deployment:
- RDF vs LPG decision documented with stakeholders
- Entity resolution rules and
sameAs/ merge policies defined - Provenance on every edge (source, timestamp)
- Schema validation on ingest (SHACL or equivalent)
- Query performance tested at 10× expected entity count
- Backup and restore procedure tested
- PII nodes classified; query-layer access control enforced
- Consumers documented (BI, API, GraphRAG, agents)
FAQs
RDF or property graphs - can I use both?
Some platforms support both (e.g., Neptune). Operationally, pick one model per domain to avoid dual maintenance.
Is GraphRAG always better than RAG?
No. Use GraphRAG when questions require multi-hop structure. Pure semantic Q&A may not need a graph at all.
References
- From Local to Global: A Graph RAG Approach (Microsoft, 2024)
- Microsoft GraphRAG Documentation
- Neo4j LLM Knowledge Graph Builder
Further Reading
Summary
- Model the problem as a graph before choosing Neo4j vs GraphDB.
- RDF and property graphs are a fork, not a staircase - commit at Step 2.
- Ontologies and enterprise practices prevent your graph from becoming a expensive hairball.
- GraphRAG is the capstone - it requires both graph discipline and RAG fundamentals.