Metadata and Ontology Management in AI Systems

Standardize definitions and structure for scalable AI data use.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course covers the principles and practices of managing metadata and ontologies to improve data interoperability, discoverability, and governance in AI systems. Participants will learn how to create standardized vocabularies and data models that enable scalable and consistent use of data across GenAI applications.

Learning Objectives

Participants will be able to:

• Understand the role of metadata and ontologies in AI data management

• Design and implement metadata standards for AI datasets

• Develop ontologies to model domain knowledge and relationships

• Apply metadata and ontology management tools for AI projects

• Enhance data integration, searchability, and governance through standardization

Target Audience

• Data architects and engineers

• AI developers and data scientists

• Knowledge managers and ontology specialists

• Data governance and compliance professionals

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Interactive lectures on metadata standards and ontology design

• Hands-on workshops for creating and managing metadata schemas

• Case studies of ontology applications in AI systems

• Group exercises on building domain ontologies and metadata catalogs

Materials Provided

• Templates and tools for metadata schema creation

• Sample ontologies and vocabulary repositories

• Guidelines for best practices in ontology management

• Case study documentation and reference materials

Outcomes

• Ability to design metadata frameworks tailored for AI use cases

• Skills to build and maintain ontologies for domain knowledge modeling

• Enhanced data consistency and discoverability in AI projects

• Improved governance and scalability of AI data assets

Outline / Content

Day 1: Introduction to Metadata and Ontologies

• Definitions, types, and importance in AI systems

• Overview of metadata standards and ontology concepts

Day 2: Designing Metadata Schemas

• Creating standardized metadata for datasets

• Tools and platforms for metadata management

Day 3: Building and Managing Ontologies

• Ontology modeling techniques and domain knowledge capture

• Using ontology editors and repositories

Day 4: Applying Metadata and Ontologies in AI

• Enhancing data integration and searchability

• Case studies and governance implications