Webinar: AI & NLP for Open-Source Archival Linked Data Workflows

Webinar Details

AI & NLP for Open-Source Archival Linked Data Workflows
Date & Time
19 Oct 23 15:00 UTC

About the webinar

Join us to learn how to leverage artificial intelligence and natural language processing to create an open source workflow for the rapid creation of archival linked data for digital collections

This webinar will cover the use of computer segmentation, computer vision, natural language processing, python scripts, and regular expressions to create Linked Data for collection items from existing metadata spreadsheets, requiring significantly fewer worker hours than manual processing and creating better results capable of rediscovering lost information about collections through the interconnectedness of Linked Data

Learning Objectives - Become familiar with tools and methods to mostly-automatically create archival linked data from existing item level descriptions - Explore the value of linked data for rediscovering details - Discover the utility of linked data as new entry points to your collections.


  • Jennifer Proctor

    Jennifer Proctor is a Faculty Research Specialist at ARLIS Defense Research Lab and a PhD Candidate in Information Science at University of Maryland College Park. Her research focuses on computational approaches to managing historical collections, with projects relating to managing millions of multilingual MARC records and R&D of tools for improving the declassification process of US government records.