The Practical Knowledge Graph Guide
Structure Your Data, Sharpen Your AI
Knowledge Graph Practical Guide | GraphRAG · Neo4j · RDF · Property Graph · Emotion AI
Overview
RAG alone won't make your AI smart. Relationships only emerge through structure — Knowledge Graph, GraphRAG, Neo4j, RDF, Property Graph, Tree-sitter, MCP, and Emotion AI. The practical guide to giving AI true reasoning through structured data.
What you will be able to do
- Choose between RDF, Property Graph, and GraphRAG for each use case
- Build a real production knowledge graph with Neo4j
- Convert codebases into knowledge graphs with Tree-sitter
- Design AI access to knowledge graphs through MCP
- Apply knowledge graphs to new domains like Emotion AI
Who is this book for
- [RAG Practitioner] Hit the ceiling with vector search alone
- [AI Agent Developer] Want to structure context relationships
- [Data Engineer] Need to operate Neo4j / Property Graph in production
- [Codebase Analyzer] Want to use Tree-sitter for AST work
- [Emotion AI / Psychology] Want to apply graphs to less-explored domains
Problems this book solves
- RAG implementation scatters info — AI can't synthesize answers
- Tried Neo4j but design guidance is unclear
- Hear about GraphRAG but don't understand how it differs from regular RAG
- Want to graph the codebase but tool choice is overwhelming
- Confused choosing between RDF and Property Graph
- Want to apply graphs to Emotion AI / psychology but few examples exist
Where this book stands
- Implementation-focused (concrete Neo4j / RDF / Tree-sitter examples)
- Cross-domain integration (GraphRAG + code analysis + Emotion AI in one book)
- Intermediate (graph DB basics assumed)
- For RAG graduates (the next step after vector search hits its limits)
Why this book
- One of the few books explaining GraphRAG at implementation level
- Clear guidance on RDF vs Property Graph trade-offs
- Open pipeline: code AST → knowledge graph via Tree-sitter
- MCP integration to make graphs queryable from AI
- Original lens: structuring approach for Emotion AI
How this differs from other AI books
| Compared to | This book's difference |
|---|---|
| Neo4j tutorials | Not Neo4j alone. Goes into GraphRAG, code analysis, and MCP integration. |
| RAG intro books | Focused on GraphRAG — what to do after vector search hits its limits. |
| Semantic Web / RDF books | Not academic-only. Practical RDF vs Property Graph trade-off guidance. |
Table of contents
- 01 Preface Free preview
- 02 Why Knowledge Graphs Now Free preview
- 03 RDF vs Property Graph Free preview
- 04 Neo4j Fundamentals
- 05 Cypher / SPARQL Query Design
- 06 What is GraphRAG
- 07 GraphRAG Implementation Patterns
- 08 Codebase to Graph with Tree-sitter
- 09 MCP Integration
- 10 Knowledge Graph × LLM Design
- 11 Emotion AI Application
- 12 Enterprise Operations
- 13 Visualization and Debugging
- 14 Benchmarking and Evaluation
- 15 The Future
- 16 Afterword
Vector search hands AI knowledge, not relationships. “Alice reports to Bob, who runs project C” is a graph fact, not a vector fact.
This book is the field guide to giving AI that structured intelligence: Neo4j, RDF, Property Graphs, GraphRAG, Tree-sitter for code ASTs, MCP integration, and even Emotion AI. All turned into patterns you can ship.
“Data gets smart not as vectors, but as graphs.”
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