Day 1

Semantic Layer by Design: An Immersive One-Day Training

An exclusive training opportunity to accelerate the design and implementation of a semantic layer within your organization.

On Day 1 of the Semantic Layer Symposium 2026, join EK’s expert instructors for a hands-on, end-to-end enterprise semantic layer design training — delivering the most interactive and practitioner-focused experience in the field.

Over the course of the day, EK’s semantic solution experts will take you through each step of a successful semantic layer program. The training will detail the semantic layer framework and its components, specifically delving into ontologies, taxonomies, and knowledge graphs, as well as their pivotal roles in unlocking advanced semantics to bridge knowledge, content, data, and AI.

Grounded in EK’s Semantic Layer Framework

The same methodology being published in Bridging Knowledge, Data, and AI (Tesfaye, Hilger & Wahl) — refined through real-world implementations at the world’s most knowledge-intensive organizations, and now available as a full-day simulation experience.

Through hands-on exercises, attendees will learn foundational and advanced concepts in ontology design and knowledge graph integration, along with practical applications to improve data interoperability and enhance enterprise systems such as advanced search, content personalization, and AI-driven applications like chatbots, recommendation engines, and AI agents. The training will examine the practical implementation of semantic technologies, using real-world case studies and leading software products to illustrate how a semantic layer can be leveraged for data transformation and integration.

What You Will Learn

Semantic layer fundamentals & AI-readiness

Understanding taxonomies, ontologies, and LPG and RDF-based models, and how to design them for AI-ready content and downstream LLM integration.

Building & implementing knowledge graphs

Hands-on exercises to model, integrate, and query semantic data using the core Semantic Web standards: RDF, OWL, SKOS, SPARQL, and SHACL validation.

AI & machine learning integration

Leveraging semantic inference, auto-tagging, and graph machine learning techniques, with an eye toward agentic AI integration.

Use cases & business applications

Exploring real-world implementations across industries, from AI agent enablement and context engineering to 360-degree data and asset views.

Semantic Layer Symposium training session

Learn by doing — not by watching.

Every module includes hands-on exercises, breakouts, and
real-world case studies led by EK’s practitioners.

Training Overview

9:00
Check-in & Registration
30 min
9:30
Enterprise Semantic Layer Kickoff

Establish a shared foundation and select the live enterprise scenarios that will anchor your hands-on work for the rest of the day.

60 min · 3 sessions
Module A
Introductions

Kick off the workshop by connecting with fellow leaders and experts. Through structured introductions, we’ll explore what we share across industries and roles, exchange perspectives, and set a foundation for the collaborative activities ahead.

The Enterprise Semantic Layer
Primer

Examine the evolution, key components, and architecture of modern semantic layers. This session defines core concepts, clarifies how semantic layers enable enterprise AI, and addresses common misconceptions, including how a Semantic Layer differs from a Metrics Layer.

Case Studies: Three Emerging Implementation Themes
Primer

Analyze three primary drivers for enterprise implementation: building a unified 360-degree business view, codifying institutional knowledge, and delivering semantic data products. We will ground these themes in real-world case studies, then vote as a group to select the live scenarios that will anchor your hands-on exercises for the rest of the workshop.

10:30
Coffee Break
30 min
11:00
Use Case Definition and Modeling

Translate business drivers into concrete requirements, competency questions, and functional use cases.

90 min · 4 sessions
Module B
Introduction to Implementation Components
Primer

Review the core building blocks required to implement an enterprise Semantic Layer and see how these technical and organizational components fit together to support broader business goals.

Competency Question and Use Case Definition
Primer

Learn how to translate high-level business goals into precise competency questions and use cases. Discover how framing these requirements early guides effective semantic modeling and demonstrates clear ROI to stakeholders.

Review of Semantic Layer Components and the Semantic Model
Activity

Examine a supplied sample conceptual model and set of use cases based on your scenario. Working in small groups, evaluate the model structure, provide direct feedback on its design, and discuss practical implementation questions.

Realizing a Successful Implementation
Activity

Move from conceptual models to practical reality. Collaborate with peers to identify immediate quick wins, expose hidden implementation risks, challenge assumptions about what might fail, and determine what your organization truly needs to achieve a successful implementation.

12:30
Lunch
90 min
14:00
Architecture and Implementation

Design a robust technical architecture tailored directly to the model requirements and use cases reviewed in the previous Module.

90 min · 3 sessions
Module C
The Enterprise AI Race
Activity

Participate in a fast-paced warm-up scenario examining how different architectural choices directly impact an organization’s speed and capability when deploying enterprise AI.

Architectural Patterns
Primer

Evaluate five technical architectural patterns for the Semantic Layer: metadata-first, centralized, hybrid, RDF/LPG, and combinations like Snowflake with a Semantic Layer. Each pattern is illustrated through real-world case studies highlighting trade-offs and implementation considerations.

Design a Practical Architecture
Activity

Apply these architectural patterns to your ongoing case study. Working in small groups, design a high-level technical architecture built specifically to support the semantic model and use cases examined in the previous Module, followed by a group debrief.

15:30
Coffee Break
30 min
16:00
Operating Model and Scale

Build the governance, team structures, and operational workflows needed to run and scale your new technical architecture.

90 min · 4 sessions
Module D
The Costs of Staying the Course
Activity

Examine the organizational risks of maintaining current operating models for enabling AI. Learn to spot early warning signs, operational bottlenecks, and governance friction that threaten a successful implementation.

The Operating Model
Primer

Learn how to structure an operating model around a functional “knowledge supply chain”. Understand the different organizational and operational components required to deliver reliable institutional knowledge to AI solutions.

Organizational Scenarios
Activity

Address real-world organizational dynamics using your team’s ongoing scenario and architecture. Develop operating model adjustments for strong, shared, or ambiguous AI mandates. Present your team’s proposed operating model design to receive direct feedback from an expert panel of industry leaders.

Closing

Reflect on the day’s progression, summarizing key architectural and operational takeaways as the symposium prepares for Day 2.

17:30
Happy Hour

Join fellow participants and facilitators for informal networking and continued discussion at one of Vienna’s most celebrated cocktail bars, a short walk from the venue.

Business Outcomes

Build an AI-ready knowledge & data foundation

Organizations that invest in semantic layers today are the ones whose AI initiatives actually deliver tomorrow. Learn to design scalable, adaptive semantic architectures built for LLM integration, agentic AI pipelines, and evolving business needs — serving as the connective tissue between your knowledge, content, data, and AI systems.

Make better decisions, faster

You can’t act on data you don’t trust. Semantic structure is the path to more reliable data for your organization. Learn how to design and interpret knowledge structures that surface the right information, in the right context, at the right time — enabling smarter business intelligence and more trustworthy AI outputs.

Drive operational efficiency & quantifiable ROI

A semantic layer can cut the time employees spend searching, consolidate redundant software licenses, and eliminate duplicate research and data purchases. Learn how your organization can achieve this through hands-on ontology design and knowledge graph implementation.

Who Should Attend

Organizational leaders

CDOs, CDAIOs, and business executives looking to enhance critical insights through semantics and data standards.

Data modelers, scientists & engineers

Data modelers, scientists, and engineers involved in AI, machine learning, and data modeling initiatives.

Knowledge & data managers

Professionals seeking to understand how ontologies and knowledge graphs drive measurable business value.

IT professionals

IT professionals interested in implementing and managing semantic technologies at scale.

Day 2

Conference Agenda

Register Now!