Semantics in the Age of AI: Questions Practitioners Must Answer in 2026

Context and challenge

As AI systems become the primary way people and applications access information, the semantic layer (ontologies, knowledge graphs, and contextual metadata) has moved from nice‑to‑have to critical infrastructure. Yet many organisations still struggle to decide when to invest in graphs, how to manage change, and how to align semantic work with data and AI teams.

Meeting the challenge

Join our speakers to explore the ideas, insights and provocations from the morning’s sessions.

Over the past three years, the AI conversation has evolved from Generative AI to AI-ready data, and now to agentic AI and autonomous business. Yet one fundamental truth has remained constant: AI is only as effective as the data that grounds it.

As organizations move beyond chatbots to intelligent agents capable of orchestrating business processes, the need for well-organized, well-described enterprise data has never been greater.

Early Lessons from a Work in Progress

The Challenge

Your chance to visit the exhibits and network with fellow attendees.

Organizations are increasingly evaluating large language models (LLMs) for content classification and tagging. While LLMs offer flexibility and can accelerate semantic model development, they also introduce considerations around consistency, explainability, governance, and cost.

Libraries have cataloged the world's published record for over a century, but that description sits as text inside records rather than as identifiable things. We have spent the past several years rebuilding it as a knowledge graph. WorldCat Entities publishes descriptions of people, events, places, organizations, and works as persistent, resolvable URIs. The Dewey Decimal Classification is published on the same basis, which turns a familiar classification scheme into a machine-navigable hierarchy of human knowledge.