Module 2 — Ontological Foundations & Graph Construction (LlamaIndex + Neo4j)

▶ NEXT IN THE SERIES — Module 3 — Hierarchical Community Indexing & Global-Local Search (Microsoft GraphRAG & DRIFT)    • Module 3 — Hierarchical Community Indexing...   ============================================================ Advanced GraphRAG & Knowledge Agents — a friendly, in-depth course on building structured, graph-based retrieval systems, from first principles to production multi-agent pipelines. MODULE 2 — Ontological Foundations & Graph Construction Methodologies How do you turn raw documents into a queryable knowledge graph? We compare the two dominant construction paradigms and build one hands-on. In this module: • OpenIE triple extraction vs. hierarchical Leiden community clustering — accuracy, token cost, and noise • Schema-constrained extraction to tame hallucinated nodes • A quick intro to LlamaIndex and Neo4j before the code • Lab: build a schema-typed property graph (PropertyGraphIndex + SchemaLLMPathExtractor) AND cluster it into communities (Neo4j GDS Leiden) This is Module 2 of 8 in the "Advanced GraphRAG & Knowledge Agents" series — watch them in order. Course roadmap: 1. The RAG Bottleneck & Evolution of Graph-Based Retrieval 2. Ontological Foundations & Graph Construction (LlamaIndex + Neo4j) 3. Hierarchical Community Indexing & Global-Local Search (Microsoft GraphRAG & DRIFT) 4. Dual-Level Retrieval & Incremental Updates (LightRAG) 5. Neurobiologically Inspired Long-Term Memory (HippoRAG) 6. Relation-Free Hierarchical Graph Architectures (LinearRAG) 7. Minimum Cost Subgraphs & Inference-Time Structuring (AGRAG & StructRAG) 8. Multi-Tiered Architectures, Workflows & Benchmarks (GraphRAG-Bench) #GraphRAG #RAG #KnowledgeGraphs #LLM #AI #VectorDatabase #RetrievalAugmentedGeneration #MachineLearning #Neo4j #AIEngineering