Semantic Data New York 2026
Program Update: First Sessions Announced
This year we’re introducing a number of interactive sessions where attendees can ask questions and explore key issues with others in the room. Designed to complement the wider program, they create space for open exchange and shared thinking.
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
This collective roundtable discussion focuses on answers to questions that increasingly determine whether semantic initiatives deliver real value or remain science projects.
Join the conversation, share your experiences and learn what others are doing.
Moderator: Dr. Robert Sanderson, Senior Director for Digital Cultural Heritage, Yale University
LLM, Semantic Classification, or Both? Designing the Right Approach for Enterprise Data
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.
This session compares LLM-driven classification, semantic classification, and hybrid approaches that combine the strengths of both. Through practical demonstrations and real-world examples, attendees will see how AI-assisted modeling can accelerate semantic model creation, while semantic classification delivers consistent, repeatable tagging across large volumes of enterprise content.
There will also be an examination of an often-overlooked architectural consideration: cost predictability. As document volumes grow, content is reprocessed to meet new business requirements, and AI licensing models evolve, the cost of token-based classification can become increasingly difficult to forecast.
Semantics, the ‘DNA’ of Biotechnology Data
Context
- The biotechnology industry has embraced semantic data in the form of taxonomies and ontologies for several decades. Examples include the Gene Ontology, dating to 1998, and the Cell Ontology, which was initially released in 2004.
- While the use of semantic data on the research and development side of biotechnology is well established, changes in the semantic industry on the business side are evolving more rapidly.
- Many biotechnology companies face the same challenges as other industries when it comes to data:
- Siloed systems with standalone metadata structures.
- Using the wrong systems to manage data and assets.
- Inadequately managed data for use in reporting and analytics.
With the sudden growth and adoption of artificial intelligence, ontologies, knowledge graphs, and semantic layers are being viewed with renewed interest in biotechnology business operations.
Covering
- How semantics is used in the biotechnology industry.
- How artificial intelligence is driving semantic adoption to meet marketing use cases.
- The general relevance of what the biotech industry is learning and doing for other managers of semantic data.
Ahren Lehnert, Senior Taxonomist, Genentech
More to follow….
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