Linked Data and AI Part 1: A Primer

This course explores how generative AI can support linked data creation in libraries. We’ll cover foundational concepts including entities, relations, and semantic web technologies. We will briefly explore historical continuities with symbolic AI and knowledge representation, and contrast these with modern generative AI approaches.

The course examines how the reasoning and expressiveness tradeoffs manifest in practical metadata work, particularly through grounded integrations within the BIBFRAME ontology, Library of Congress BIBFRAME Linked Data Service, and BIBFRAME metadata application profiles.

Through lectures and a generative AI case study of linked data creation, students will gain practical understanding of how to navigate reasoning and expressiveness tradeoffs when applying AI to bibliographic data. Students will learn about current experiments and tools that leverage generative AI while maintaining connections to formal knowledge structures.

Prerequisites

No technical background is required, though prior experience with linked data is helpful.

What You’ll Learn

• Foundational concepts: entities, relations, and semantic web technologies
• Historical continuities between symbolic AI and modern generative AI approaches
• Reasoning and expressiveness tradeoffs in metadata work
• Practical applications within BIBFRAME ontology and Library of Congress BIBFRAME Linked Data Service
• Current experiments and tools leveraging generative AI for bibliographic data
• How to maintain connections to formal knowledge structures while using AI