Share
))
HBO and The Ringer's Bill Simmons hosts the most downloaded sports podcast of all time, with a rotating crew of celebrities, athletes, and media staples, as well as mainstays like Cousin Sal, Joe House, and a slew of other friends and family members who always happen to be suspiciously available.
…
continue reading
Manage episode 523994505 series 3705596
Content provided by Keith Bourne. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Keith Bourne or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/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 episodes
