2026 Speakers

Mike Pool (Bloomberg)
Semantic Technology Product Manager

Ora Lassila (Amazon Neptune
Principal Technologist

Martin Romacker (Roche)
Product Manager – Roche Data Marketplace

Model-driven Application Design based on a FAIR Semantic Metadata Registry

As enterprises race to scale AI, the governance of high-quality metadata has become the critical bottleneck. To build a truly solid foundation, organizations must move beyond disparate digital repositories toward a unified semantic layer. This presentation explores how implementing a semantic metadata registry establishes business-layer connectivity by applying FAIR (Findable, Accessible, Interoperable, Reusable) principles at the point of design.

We will detail the capabilites of the Roche Terminology and Interoperability System (RTIS), a platform that synchronizes terminology, metadata, and ontology engineering. We will expose our “hybrid governance” model: a centralized approach for core metadata inventory paired with federated freedom for data domains. This balance provides the agility needed for domain-specific design while maintaining enterprise-wide interoperability. Attendees will learn how crafting semantic schemas—combining standardized elements with application-specific contexts—enables powerful model-driven UI and API generation, turning governance from a hurdle into an accelerator for digital transformation.

Ryan Chandler PhD (AbbVie)
Graph Engineer

Towards the Cognitive Enterprise, Data-Centric Architecture and the Impact of Knowledge Graphs at AbbVie

This presentation will share AbbVie’s journey implementing a data-centric architecture in the form the AbbVie R&D Convergence Hub, ARCH knowledge graph. We will share the journey from a time-saving harmonization of key knowledge to a comprehensive R&D platform that is the core resource for target identification, drug repurposing, asset prioritization, and approaches to combination therapies. We will also share our current development and future plans for enabling automated research tools and other enterprise graph initiatives currently in flight.

Key Points:

• Business Challenge: How fragmented data silos were limiting AbbVie’s ability to accelerate the drug development pipeline.
• Solution Implementation: The architectural decisions behind AbbVie’s knowledge graph implementation, including semantic modeling approaches that enabled connection of previously disparate data sources.
• Practical Lessons: Key implementation challenges AbbVie overcame and best practices for organizations considering similar transformations.
• Future Roadmap: How AbbVie is expanding knowledge graph capabilities to drive additional business value across the enterprise and toward the development of a cognitive enterprise.

Why This Matters:

This case study directly addresses the symposium’s focus on marrying business outcomes with semantic implementation. Attendees will gain practical insights into how knowledge graphs deliver tangible results in complex data environments while supporting AbbVie’s mission to discover and deliver innovative therapies for serious health challenges.

Ben Gardner (AstraZeneca)
R&D Lead for Data Mesh & Semantic Infrastructure

Nick Lynch (Curlew Research)
Founder

Sharing antiviral discovery data to accelerate therapeutic discovery in a resource-limited environment

Open science data sharing is essential for making the most of limited global investment in drug discovery for pandemic preparedness and endemic disease. Drug discovery is an incredibly expensive enterprise, where the total costs of failed discovery efforts are now so high that each approved drug costs over $2.5B in R&D investment. However, an enormous amount of valuable data, resources, reagents, and materials are generated in the course of a discovery program that can accelerate or jump-start other drug discovery efforts that take differentiated paths or aid biologists aiming to better understand viral biology to probe weaknesses. The limited global investment in drug discovery for pandemics, the enormous threat posed to humanity and lack of market incentive make it essential for these global resources to be used efficiently. By sharing drug discovery data openly, we can simultaneously coordinate global discovery efforts simply by making it possible for other efforts to known when we are working on similar targets and chemical series, help nucleate differentiated drug discovery efforts by enabling other teams to save huge amounts of time and money by starting from target-enabling packages, and aid researchers in better understanding viral biology by providing them with cheap chemical probes that will help dissect biochemical pathways.

The talk will cover:

Data sharing philosophy: Drug discovery efforts generate many useful outputs besides drugs. How this is being made available as data products and assets

Deposit data in FAIR repositories, provide useful high-level indexes: How ASAP is ensuring data and resources are ultimately deposited in appropriate disciplinary FAIR (Findable, Accessible, Interoperable, and Reusable) repositories and biological sample repositories.
Lessons learned and future plans for a broader therapeutics hub.

Dougal Watt (Graph Research Labs)
CEO

The Graph Middleware Layer: Bridging Ontologies and Enterprise Systems

Data-centric architecture promises a cleaner, more sustainable approach to enterprise data management. But even well-designed ontologies face a persistent challenge: how do you actually connect them to the systems, teams, and workflows that run the business?

This presentation introduces the concept of a graph middleware layer – a set of generated components that bridge the gap between semantic models and enterprise infrastructure. Using declarative ontology specifications, we will explore tools to produce REST APIs, React applications, and MCP servers that give engineering teams familiar development tools, languages and integration points without requiring graph expertise. The result is an ontology that models the business domain as well as actively participates in the enterprise technology stack.

We’ll explore how this middleware approach addresses common adoption barriers: developers get standard APIs they already know how to consume; operations teams deploy via Kubernetes and CI/CD pipelines; business users interact through generated applications rather than query languages. Meanwhile, ontologists retain full control of the semantic model, with all downstream artifacts staying aligned by design.

The session draws on ongoing work on a real implementation: a GIST-aligned FHIR R5 medications solution for the New Zealand health system. We’ll show how the middleware layer enabled rapid iteration, seamless legacy integration, and controlled LLM access via MCP – all generated from a single ontology source.

Data-centric thinking is key but the missing piece has been a practical path from ontology to operational systems. The graph middleware layer provides that path.