The position up to now:

For a long time much of HealthStream’s’ taxonomy has remained static, hard coded into production environments that couldn’t be updated without development efforts. As a result, changes have not been made in years due to competing priorities.  As part of its evolution into a platform solution HealthStream is beginning to offer API services for taxonomy. 

The challenge now faced:

Preparing data for AI is less about chasing the latest models and more about building the right foundation.  This involves creating robust, extensible platforms that unify and curate enterprise data for responsible, scalable use. While legacy systems were designed for specific, narrow needs, modern data platforms must be adaptive—capable of handling change, supporting governance, and enabling flexible, AI-driven architectures.

Overview
A career spent working with photos, videos, music, and books has provided me ample opportunities to experiment with AI solutions for content tagging and metadata generation. From counting the number of people in a photo to applying keywords to kittens to writing fun facts about pop stars, AI-powered tools can perform a lot of mundane tasks. I’ve evaluated the work of these “robots” for over a decade, and I’ve recommended ways to leverage automation for scale, while still adhering to the level of quality demanded by human end users.

Effective knowledge orchestration demands more than data aggregation; it requires semantic coherence across diverse sources.

What it is:

The Ontology Pipeline is a framework for building a semantic knowledge infrastructure, with iterative steps to guide the process. 

What it does:

Refresh over coffee. Share experiences.

Networking prompt and let’s ‘break the ice’ - connect with someone you’ve not met before! Mingle with your peers. Take time to forge a new connection in your DAM and Semantic Data community.

Exhibition - your chance to check out the live demos, to contrast and compare. Talk to those who have the solution you might need.

Foundations matter. 

Data quality is one of the most important pillars of a Semantic Architecture. Regardless of our particular projects or roles well defined semantics are only valuable if there is accurate, representative, and timely data. Unfortunately, too many organizations are unable to (or shouldn’t) trust their data enough to create the information experiences where people learn, make decisions, or connect. 

Practical guidance on the art of tagging

• How taxonomy and other metadata tags help improve AI responses 
• How to identify areas of potential AI hallucinations 
• How to increase the accuracy of AI outputs. 

Context:

Language is a tool. From our first grunts, whoops, and mutterings as Homo sapiens, we have used language to communicate - but also to persuade and control. In a world of growing complexity, uncertainty, and fractaled meaning, we still attempt to organize and find meaning through the expression of language.

Perceived meaning is often dependent on the language we use. With language we can misinform, but we can also use it to create order and stability.