Ontology & Knowledge Graph Cookbook
Master semantic modeling, knowledge graph construction, and LLM integration in this 9-week advanced course.
Course Overview
This course covers the complete knowledge graph lifecycle, from basic ontology engineering to production deployment.
Curriculum
Phase 1: Foundation - Semantic Modeling (Week 1-3)
Building the theoretical foundation with ontology engineering and semantic web standards.
- Week 1: History of knowledge representation, upper ontology, domain ontology design
- Week 2: RDF/RDFS, SPO triples, URI schemes, rdflib practice
- Week 3: OWL, class hierarchy, restrictions, reasoning with Protégé
Phase 2: Data Engineering - Knowledge Graph Construction (Week 4-5)
Building knowledge graphs through extraction pipelines and graph databases.
- Week 4: NER, Relation Extraction, Entity Resolution with LLM
- Week 5: Neo4j, LPG modeling, Cypher query optimization
Phase 3: LLM Integration - GraphRAG & Agents (Week 6-7)
Combining knowledge graphs with LLMs for advanced reasoning.
- Week 6: Hybrid Vector + Graph retrieval, sub-graph context injection
- Week 7: Ontology-guided action planning, multi-agent collaboration
Phase 4: Production & Deployment (Week 8-9)
Real-world projects and production deployment.
- Week 8: Domain-specific case studies (Medical, Legal, Finance)
- Week 9: Graph visualization, API servers, performance optimization
Learning Outcomes
After completing this course, you will be able to:
- Design and implement domain ontologies using OWL
- Build knowledge extraction pipelines with LLM
- Store and query knowledge graphs with Neo4j
- Implement GraphRAG for enhanced LLM responses
- Design ontology-based agent systems
- Deploy production-ready knowledge graph services
Prerequisites
- Python intermediate level
- Basic understanding of SQL
- Familiarity with LLM APIs (optional)