Challenges for the ambitious:

Organizations with semantic ambitions such as dynamic content and personalization often face practical challenges in implementing knowledge organization structures that are sophisticated enough to support them. These challenges include system constraints, fledgling governance models, and human factors.

Some solutions:

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.

How DITA can be used to break content down into logical, granular chunks to be used across publications and delivery channels
A useful tool on the road to semantics

DITA in the delivery of an optimal end user experience:

Background

Over the past five years, Yale University has built an innovative knowledge graph that connects its libraries, archives and museums in a single discovery platform, advancing the university’s core missions of teaching and research excellence. 

Led by Conference Chair, Madi Weland Solomon, and with insights from the event’s speakers, this interactive roundtable is an opportunity to exchange views, share experiences, hear innovative questions, pose questions and expand one’s network - just the way to kickstart the afternoon. 

All in a collaborative environment designed to increase knowledge, enhance skills, and propel careers. 

Covering (just to start): 

Time to have some lunch and visit the exhibition. See you back at 2:00 for the start of the afternoon sessions!

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: