T13: How to Implement Semantic Layer Architectures
Monday, May 5, 2025
01:45 PM - 05:00 PM
Intermediate
Enterprise AI’s business potential cannot be overstated; Specifically, a Semantic Layer provides Generative AI (GenAI) with a programmatic framework to make organizational context, content, and domain knowledge machine readable. By employing standards-based semantic components such as metadata, business glossaries, taxonomy/ontology, and graph solutions, a semantic layer arms organizations with a framework to aggregate and connect siloed data and unstructured content, explicitly provide business context for data, and serve as the layer for explainable GenAI solutions.
This tutorial will introduce Semantic Layer frameworks and their core components, including the business glossary, metadata, taxonomy, ontology, and knowledge graph. It will delve into how these components interconnect organizational knowledge and data assets, subsequently enhancing systems such as chatbots, recommendation engines, and intelligent search functions. Additionally, the tutorial will showcase case studies that explore the technical architectures of Semantic Layers, emphasizing the components that facilitate enterprise-scale data transformation efforts through capabilities like federated metadata management, data catalogs, ontology/knowledge graphs, and AI infrastructure. Specifically, it will examine the top four architectural patterns successfully implemented across various organizations, highlighting best practices for enabling enterprise AI and considering organizational aspects for each. This is an interactive workshop, to learn about semantic solutions and use cases, and we will actually architect one.
After attending this tutorial, participants will be able to
- Define a Semantic Layer framework
- Understand the use cases and applications of a Semantic Layer within the enterprise
- Deconstruct the components of a semantic layer architecture and supporting technical ecosystem
- Identify the right enterprise AI opportunities and use cases for semantic layer solutions
- Choose the right semantic layer architecture pattern based on the organizational considerations
- Adopt the best practices for the chosen architectural pattern to operationalize scalable semantic layer solutions
Lulit Tesfaye is a Partner and the VP for Knowledge & Data Services and Engineering at Enterprise Knowledge, LLC., the largest global consultancy dedicated to Knowledge and information management. Lulit brings over 15 years of experience leading diverse information and data management initiatives, specializing in technologies and integrations. Lulit is most recently focused on employing advanced Enterprise AI and semantic capabilities for optimizing enterprise data and information assets.
Urmi Majumder is a Principal Consultant in the Advanced Data and Enterprise AI practice at Enterprise Knowledge where she leads system architecture, design, and implementation of a broad range of enterprise solutions. She has 15 years of experience leading the development of technical solutions in support of a wide variety of federal and commercial clients by integrating open-source, SaaS, and COTS tools, as well as establishing the connection between these tools and their business users. Her diverse portfolio includes the design and development of data-centric solutions including content management systems, record management systems, knowledge portals, search applications, semantic applications, data catalogs, and AI/ML applications, both in the context of new system development and data modernization efforts.