Episode Details

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, a knowledge architect, community builder, and host of the Knowledge Graph Insights podcast. They explore the relationship between knowledge graphs and ontologies, why these technologies matter in the age of AI, and how symbolic AI complements the current wave of large language models. The conversation traces the history of neuro-symbolic AI from its origins at Dartmouth in 1956 through the semantic web vision of Tim Berners-Lee, examining why knowledge architecture remains underappreciated despite being deployed at major enterprises like Netflix, Amazon, and LinkedIn. Swanson explains how RDF (Resource Description Framework) enables both machines and humans to work with structured knowledge in ways that relational databases can't, while Alsop shares his journey from knowledge management director to understanding the practical necessity of ontologies for business operations. They discuss the philosophical roots of the field, the separation between knowledge management practitioners and knowledge engineers, and why startups often overlook these approaches until scale demands them. You can find Larry's podcast at KGI.fm or search for Knowledge Graph Insights on Spotify and YouTube.

Timestamps

00:00 Introduction to Knowledge Graphs and Ontologies
01:09 The Importance of Ontologies in AI
04:14 Philosophy's Role in Knowledge Management
10:20 Debating the Relevance of RDF
15:41 The Distinction Between Knowledge Management and Knowledge Engineering
21:07 The Human Element in AI and Knowledge Architecture
25:07 Startups vs. Enterprises: The Knowledge Gap
29:57 Deterministic vs. Probabilistic AI
32:18 The Marketing of AI: A Historical Perspective
33:57 The Role of Knowledge Architecture in AI
39:00 Understanding RDF and Its Importance
44:47 The Intersection of AI and Human Intelligence
50:50 Future Visions: AI, Ontologies, and Human Behavior

Key Insights

1. Knowledge Graphs Combine Structure and Instances Through Ontological Design. A knowledge graph is built using an ontology that describes a specific domain you want to understand or work with. It includes both an ontological description of the terrain—defining what things exist and how they relate to one another—and instances of those things mapped to real-world data. This combination of abstract structure and concrete examples is what makes knowledge graphs powerful for discovery, question-answering, and enabling agentic AI systems. Not everyone agrees on the precise definition, but this understanding represents the practical approach most knowledge architects use when building these systems.
2. Ontology Engineering Has Deep Philosophical Roots That Inform Modern Practice. The field draws heavily from classical philosophy, particularly ontology (the nature of what you know), epistemology (how you know what you know), and logic. These thousands-year-old philosophical frameworks provide the rigorous foundation for modern knowledge representation. Living in Heidelberg surrounded by philosophers, Swanson has discovered how much of knowledge graph work connects upstream to these philosophical roots. This philosophical grounding becomes especially important during times when institutional structures are collapsing, as we need to create new epistemological frameworks for civilization—knowledge management and ontology become critical tools for restructuring how we understand and organize information.
3. The Semantic Web Vision Aimed to Transform the Internet Into a Distributed Database. Twenty-five years ago, Tim Berners-Lee, Jim Hendler, and Ora