))
Ontology-Based Knowledge Engineering for Graphs (Chapter 13)
Manage episode 523994505 series 3705596
Contenuto fornito da Keith Bourne. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Keith Bourne o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
Ontologies are the semantic backbone that enable AI systems to reason precisely over complex domain knowledge, far beyond what vector embeddings alone can achieve. In this episode, we explore ontology-based knowledge engineering for graph-backed AI, featuring insights from Keith Bourne's Chapter 13 of *Unlocking Data with Generative AI and RAG*. Learn how ontologies empower multi-hop reasoning, improve explainability, and support scalable, production-grade AI systems.
In this episode:
- The fundamentals of ontologies, OWL, RDFS, and Protégé for building semantically rich knowledge graphs
- How ontology-based reasoning enhances retrieval-augmented generation (RAG) pipelines with precise domain constraints
- Practical tooling and workflows: from ontology authoring and validation to Neo4j graph integration
- Trade-offs between expressivity, performance, and maintainability in ontology engineering
- Real-world use cases across finance, healthcare, and compliance where ontologies enable trustworthy AI
- Open challenges and future directions in ontology automation, scalability, and hybrid AI systems
Key tools and technologies mentioned:
- Protégé (ontology authoring and reasoning)
- OWL 2 DL (Web Ontology Language for expressive domain modeling)
- RDFS and SKOS (vocabularies for annotation and lightweight semantics)
- Neo4j (graph database for knowledge graph storage and traversal)
- OWL reasoners (Pellet, HermiT, Fact++)
Timestamps:
00:00 – Introduction and episode overview
02:30 – Why ontologies matter now in AI and RAG
05:15 – Ontology basics: classes, properties, and logical constraints
08:00 – Tooling walkthrough: Protégé, OWL, Neo4j integration
11:45 – Performance and production considerations
14:30 – Real-world applications and case studies
17:00 – Technical trade-offs and best practices
19:15 – Open problems and future outlook
Resources:
- "Unlocking Data with Generative AI and RAG" by Keith Bourne – Search for 'Keith Bourne' on Amazon and grab the 2nd edition
- Visit Memriq AI for tools and resources: https://memriq.ai
22 episodi
Condividi
