How AI Helps Reveal Hidden Digital Collections

Libraries have invested heavily in digitization. The next challenge is discovery.

Libraries, archives, and cultural heritage institutions have spent years digitizing collections, preserving unique materials, and making them available online. Millions of photographs, manuscripts, newspapers, audiovisual recordings, and archival documents are now accessible in digital form.

But digitization is only part of the journey. Making collections available does not automatically make them discoverable.

Digital collections create the greatest value when researchers can easily find, understand, and connect the materials they need. Whether supporting academic research, preserving local history, or showcasing unique special collections, libraries are increasingly expected to provide discovery experiences that go beyond simple keyword searches. Today’s users expect to uncover meaningful relationships between people, places, events, and organizations, even when those connections were never explicitly described in the original metadata.

A photograph may include important historical figures whose names never appear in the metadata. A manuscript may mention organizations, locations, or events that remain hidden within the text. Researchers can only find what has been described, and creating those descriptions manually is often one of the most time-consuming aspects of digital collection management.

As collections continue to grow, libraries face a familiar question:

How can we expose more of our collections without dramatically increasing manual effort?

This is where AI-assisted entity extraction can help.

Entity extraction helps identify meaningful information within digital content, such as people, locations, and organizations, and present those suggestions to librarians and archivists for review. Rather than requiring staff to manually review every item and identify every relevant entity, AI can assist by recognizing these connections and presenting them for review by a librarian.

Before entity extraction

After entity extraction

The value of entity extraction extends far beyond efficiency.

AI-assisted entity extraction helps transform metadata into richer research and discovery experiences. By uncovering relationships and context that might otherwise remain hidden, it enables libraries to expose more of the stories contained within their collections.

Imagine a researcher exploring a digital archive of historical photographs, personal papers, or oral histories. Instead of relying solely on manually created metadata, they can discover connections between people, places, organizations, and events that appear across multiple collections. These relationships provide valuable context, making it easier to uncover relevant materials and supporting new avenues for research that may not have been possible through traditional metadata alone.

For libraries, this means moving beyond simply describing collections toward helping users explore them more intuitively. Richer metadata supports better discovery, deeper engagement, and more meaningful interactions with unique cultural and scholarly resources. Rather than focusing only on operational efficiency, AI-assisted enrichment helps institutions unlock the full research potential of their digital collections.

For researchers, this can transform the discovery experience. Instead of searching only through existing metadata, users can uncover connections across collections based on people, places, organizations, and themes mentioned within the content itself.

For librarians and archivists, entity extraction provides another way to enrich collections while maintaining oversight and control. AI-generated suggestions can be reviewed, refined, and approved before they become part of the collection record.

This approach allows institutions to expand access without sacrificing quality.

“A key factor in selecting Alma Specto was its AI Digital Metadata Assistant and entity management features, which streamline staff workflows, improve metadata accuracy, and support interoperability across systems. The AI Digital Metadata Assistant detects people, objects, and places within digital files and presents them to metadata librarians for review and linkage to authoritative entities in controlled vocabularies. Metadata librarians can accept, modify, or reject proposed entities and suggested metadata, enhancing accuracy while reducing labor intensive processes.”
– Dr. Misu Kim, Assistant Library Director for Technical Services, UT Dallas Libraries

This challenge is not unique to any one institution. Libraries around the world are already exploring how AI can help reveal hidden connections within their collections and improve discovery for researchers.

Helping users find those hidden connections requires more than digitization alone. It requires richer metadata, better context, and new ways of exposing information that would otherwise remain buried within collections.

This is exactly the mission behind Alma Specto. Its AI-assisted capabilities, including entity extraction, are designed to help libraries and archives improve discoverability while keeping librarians and archivists at the center of every workflow. AI-generated suggestions are always reviewed, refined, and approved by staff before they become part of the collection record.

As digital collections continue to grow, helping researchers uncover meaningful connections within that content will become increasingly important. Responsible AI offers libraries an opportunity to expose more of their collections, improve discovery, and make unique cultural and scholarly resources more accessible to the communities they serve.

Want to see how libraries are applying AI to improve discovery in their digital collections?

Join our upcoming Library Journal webinar “Reimagining Digital Collections for the Next Generation of Library Users”, where Texas Christian University and University of Texas at Dallas will share how they’re using Alma Specto AI-assisted workflows to enhance metadata, improve discoverability, and increase access to their unique collections.

Register for the webinar on September 22.