Knowledge Graph Retrieval

The chapters in this module, in reading order.

#	Chapter
00	Knowledge graph retrieval — First-principles overview
01	Flat Retrieval Failure — Why cosine similarity can't follow the map
02	Graph Data Model — Nodes, edges, and the grammar of facts
03	Knowledge Graph Construction — From raw text to typed triples
04	Graph Databases — The engine that knows how to follow relationships
05	Entity Linking — Which station does "Apple" mean?
06	Graph Embeddings — The graph embedding between distant stations
07	Graph RAG Architecture — Wiring the knowledge graph into an LLM
08	Community Detection — Mapping the districts of the knowledge graph
09	Multi-hop Reasoning — Planning a route through multi-hop junctions
10	Hybrid Graph+Vector Retrieval — The best of both maps
11	Graph Maintenance — Keeping the knowledge graph accurate over time
12	Graph Evaluation — Measuring the map, the route, and the answer separately
13	Honest Admission — Where the knowledge graph still breaks