Unlock how ontology-driven knowledge engineering transforms AI from guesswork into a trusted decision partner. In this episode, we explore why ontologies matter now, their strategic advantages for compliance and risk management, and how tools like Protégé and OWL enable explainable, multi-step AI reasoning.

In this episode:

- Understand the difference between ontology-based AI and traditional keyword/vector search

- Learn how ontologies embed domain logic for precise, auditable insights

- Explore key tools and languages: Protégé, OWL, RDFS, and Neo4j

- Discover real-world industry applications in finance, healthcare, and beyond

- Discuss challenges, governance, and best practices for ontology projects

- Hear from Keith Bourne on why ontology engineering is essential for trustworthy AI

Key tools & technologies:

Protégé, OWL (Web Ontology Language), RDFS, Neo4j graph database, Retrieval Augmented Generation (RAG)

Timestamps:

[00:00] Introduction & overview of ontology-based knowledge engineering

[02:30] The strategic advantage of ontologies vs traditional AI methods

[06:15] Why now? Business drivers and technological readiness

[09:00] Key concepts: OWL, RDFS, and semantic reasoning

[12:45] Ontology development workflow and best practices

[16:00] Benefits: improved compliance, explainability, and operational efficiency

[18:30] Challenges and governance considerations

[20:00] Real-world use cases and future outlook

Resources:

- Book: "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 AI leadership insights, practical guides, and research breakdowns