Digital illustration of data streams connecting historical figures and landmarks in a vast library.

Unlocking Hidden Insights: How Named Entity Recognition is Revolutionizing Cultural Heritage

Lena Voss in Tech & Innovation June 2025 • 4 min read.

"Dive into the world of automatic indexing and discover how named entity recognition is transforming the way we access and understand our shared cultural history."

Imagine a world where accessing centuries of cultural heritage is as simple as typing a few keywords. Thanks to extensive digitization efforts, institutions like the Norwegian Broadcasting Corporation (NRK) are making vast archives of radio and TV content available to the public. However, sifting through this mountain of data requires more than just converting analog to digital; it demands smart, automated indexing solutions.

Enter Named Entity Recognition (NER), a powerful technology that automatically identifies and categorizes key elements within text, such as people, places, organizations, and events. By leveraging NER, we can unlock deeper insights into cultural content and revolutionize how we search, discover, and interact with our shared history.

This article explores the transformative role of NER in automatic indexing, drawing insights from a pioneering research project focused on the NRK archive. We'll delve into the core questions driving this innovation, examine the methodologies employed, and uncover the exciting potential for the future of cultural heritage exploration.

Why Named Entities Matter: More Than Just Keywords

Digital illustration of data streams connecting historical figures and landmarks in a vast library.

Traditional keyword-based indexing often falls short when it comes to capturing the richness and complexity of cultural content. Named entities, on the other hand, provide a structured and nuanced way to represent the key elements within a document. Think of it this way: instead of simply tagging a news clip with "Oslo," NER can identify "Oslo" as a location, linking it to related information and providing a more comprehensive understanding of the clip's context.

The research project highlighted in the article dives deep into understanding how users, including both the general public and trained librarians, utilize named entities when searching for and describing content. The core question is whether NER can help to improve search functions by better indexing.

To what extent do users naturally incorporate named entities when searching for content?
How do trained librarians leverage named entities in their indexing practices?
Are some types of entities (e.g., people, places, organizations) more salient than others in different genres and materials?
What characteristics define a truly "salient" entity for indexing purposes?

By answering these questions, the research aims to refine NER techniques and tailor them specifically to the needs of cultural heritage archives. This will not only enhance search accuracy but also facilitate the generation of automatic recommendations, connecting users with content they might otherwise miss.

The Future of Cultural Heritage: Linked Data and Beyond

The research also explores how NER can improve the connections between related documents and data sets, linking information across different cultural institutions. This approach contributes to the growing field of Linked Data and Semantic Web technology, where entities are interconnected to create a web of knowledge. By creating richer, more interconnected archives, we can foster deeper understanding and appreciation of our shared cultural heritage for generations to come.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1145/3020165.3022166, Alternate LINK

Title: Exploring The Role Of Named Entities In Automatic Indexing

Journal: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval

Publisher: ACM

Authors: Anne-Stine Ruud Husevåg

Published: 2017-03-07

Everything You Need To Know

How does Named Entity Recognition (NER) work in automatic indexing of cultural heritage archives?

Named Entity Recognition, or NER, is a technology that automatically identifies and categorizes key information within text, like people, places, organizations, and events. In the context of cultural heritage, NER is used to automatically index archives making it easier for users to search, discover, and interact with historical content. This is more effective than traditional keyword searches because NER provides a structured and nuanced representation of the content.

Why are named entities more effective than traditional keywords when indexing cultural heritage content?

Traditional keyword-based indexing often struggles to capture the full context and richness of cultural content. Named entities, identified through Named Entity Recognition, offer a more structured and nuanced approach. For example, instead of just identifying "Oslo", NER can classify "Oslo" as a location and link it to related information, which gives a more comprehensive understanding of the content's context. NER enhances the connections between related documents and datasets by linking information across different cultural institutions.

What core questions are being explored in the research project focused on the NRK archive and Named Entity Recognition?

The research project aims to understand how users, including both the public and trained librarians, use named entities when searching for and describing content. Key questions include: To what extent do users naturally incorporate named entities when searching for content? How do trained librarians leverage named entities in their indexing practices? Are some types of entities (e.g., people, places, organizations) more salient than others in different genres and materials? What characteristics define a truly "salient" entity for indexing purposes?

How does Named Entity Recognition contribute to the development of Linked Data and Semantic Web technology within cultural heritage?

Linked Data and Semantic Web technology involves connecting entities to create a web of knowledge. Named Entity Recognition plays a crucial role in this by improving the connections between related documents and datasets, linking information across different cultural institutions. By creating richer, more interconnected archives, it can foster deeper understanding and appreciation of our shared cultural heritage for generations to come. Using Named Entity Recognition we can connect "Oslo" in the NRK archive to other mentions of "Oslo" in other archives.

What are the potential benefits of using automatic indexing and Named Entity Recognition for users exploring cultural heritage archives?

Automatic indexing using Named Entity Recognition enhances search accuracy but also facilitates the generation of automatic recommendations. By understanding the named entities within a document, the system can connect users with content they might otherwise miss. It also helps in refining Named Entity Recognition techniques, so they can be tailored specifically to the needs of cultural heritage archives. The end goal is to make cultural heritage more accessible and understandable.