Knowledge Graphs and IA
In this episode of the "Earley IA Podcast," Earley and Associates founder Seth Earley and I discuss the role of information architecture in the development of knowledge graphs, the importance of keeping "humans in the loop," and the unsung benefits of starting small with knowledge graphs (even when—especially when—you've got big plans for the next iteration).
Interview Highlights
- Language is never natural - it is always constructed, and information architecture is fundamentally the discipline of making those constructions legible to both humans and machines.
- Schema.org gives algorithms the context they need to interpret web content, enabling search to understand that "gluten-free delivery near me" means food delivery from a restaurant.
- Making internal search "like Google" requires the same content curation, semantic markup, and structural investment that organizations pour into external SEO - but almost never apply internally.
- If your taxonomy has a large miscellaneous category, your domain model is probably wrong - the structure hasn't created enough findable places for things to go.
- AI without a knowledge graph is just a machine that confidently operates without context - the KFC Germany promotion disaster and Microsoft's Tay chatbot both illustrate what happens when humans leave the loop.
- A knowledge graph is data in context, and the open world assumption means it can absorb new data sources and relationships without breaking - unlike closed relational databases that return false for anything not already defined.
- Gall's Law says every complex system that works evolved from a simple system that works - starting small with a focused, standards-based ontology is not a budget compromise, it is the strategically correct approach.
Venue
- Earley IA Podcast, Online | December 15, 2022
Listen to the Interview
