Perspectives, methods, case studies, and interviews about information architecture and structured content design for the modern web.
Underneath the hype and hyperbole of "AI" capabilities, there's a compelling use case for responsibly using LLMs to help information architecture practitioners make better use of time-tested analysis and classification approaches and tools. Here is an example of one way that LLMs and rules-based text analysis techniques can be coordinated to generate tags for a topic taxonomy that has fallen out of sync with its content set.
Two weeks ago it was again my pleasure and privilege to attend Taxonomy Boot Camp (TBC) in Washington, DC. This was TBC's 20th year, and, in the four times I've attended since 2016, was the strongest argument yet for the enduring importance of well-designed and integrated taxonomies. In addition to tracks for "taxonomy fundamentals," this year's event featured tracks for "taxonomy applications," "taxonomy operations," and "advanced data and semantic layers." This is my roundup of the themes and insights that stood out to me most.
Modern AI products make a lot of promises around the "expertise" delivered by their models. Sometimes, though, another expert is not what I (or my clients) need. “Deskilling” replaces the "magic" of an LLM "figuring everything out" with structured, predictable steps and then uses language models as the glue between natural language and computational tasks.
I helped the micro-learning education startup Elemeno Health design, develop, and release a structured content solution to more effectively create, publish, and reuse thousands of hyperlocal, clinician-focused resources at patient care locations across the country. Elemeno's content now works harder and lasts longer, allowing it to scale more effectively over time and deliver greater value to Elemeno customers.
In this episode of "The Informed Life," Jorge Arango and I discuss the role of structured content in making complex ideas clear online. Topics include the transition from traditional web pages to a data-centric web design, the role of information architecture in making complex information systems accessible and intelligible to both humans and machines, and the challenges and opportunities in using semantically sound structured content to enhance digital information's usability and accessibility.
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).
Last week I had the pleasure and privilege of attending the 18th annual Taxonomy Boot Camp in Washington DC. This year’s conference carried on its strong tradition of ideas, talks, and workshops for neophytes and experts alike, and, as always, provided much food for thought. Here’s a roundup of the themes and topics I saw recurring across talks, plus (as always) a bit of commentary on where I see them fitting into the bigger picture of designing information spaces that are easy to understand and pleasant to use.
Are you still trying to wrap your head around what structured content is, how to use it, and what the benefit is to the folks who use your content? One way to think of it is that structured content treats information as a set of ideas, concepts, and facts to communicate, each described in a way that conveys its meaning in a machine readable way. Here's a brief illustration to demonstrate what that looks like.
Domain modeling helps organizations discover the information structures and meaning that allow structured content to reach further and last longer. By uncovering the assumptions and tacit rules that plague your content ecosystem, domain modeling can help you facilitate the shared vision necessary to create resilient, scalable systems for communication across channels.
By situating data in context, knowledge graphs create valuable opportunities for content creation, recommendation, and management—even at small scales. A boutique approach to knowledge graph design offers a scalable strategy for starting out small and solving well-defined communication and discovery problems to create measurable value.