Intermediate

Knowledge Graphs — Intermediate

Build graphs from unstructured text and run them in production: entity extraction, ontology evolution, hybrid graph+vector retrieval, entity resolution, and graph-retrieval evaluation.

5 lessons ~250 min total
  1. 1

    Entity Extraction & Graph Construction

    Turn unstructured text into a graph: named-entity recognition, relation extraction, LLM-based triple extraction with structured output, coreference, and dedup-on-construction.

    50 minNERRelation ExtractionLLM Extraction
  2. 2

    Ontology Design & Schema Evolution

    Ontology vs taxonomy, class hierarchies and constraints, controlled vocabularies, and how to evolve and version a graph schema without breaking the queries that depend on it.

    50 minOntology DesignConstraintsSchema Evolution
  3. 3

    Hybrid Retrieval: Graph + Vector

    Combine vector similarity with graph traversal: vector-seed-then-expand and graph-filter-then-rank patterns, local vs global GraphRAG, and assembling subgraph context for an LLM.

    50 minHybrid RetrievalVector + GraphSubgraph Search
  4. 4

    Entity Resolution & Deduplication

    Why duplicate entities wreck a graph, and how to fix them: record linkage, blocking, similarity scoring, match clustering, and safely merging nodes while re-pointing their edges.

    50 minEntity ResolutionRecord LinkageDeduplication
  5. 5

    Evaluating Graph Retrieval

    The intermediate capstone — measure graph and GraphRAG retrieval quality with gold subgraphs, subgraph recall, path correctness, and answer faithfulness, then run the tuning loop.

    50 minGraph MetricsGold SubgraphsEvaluation