AI in Cataloguing & Metadata Services: Automation, Accuracy, and the Future of Library Technical Processing

By Niranjan Mohapatra, World Skill Center
Series: AI Transformation in Libraries (Part 3 of 10)

Introduction

Cataloguing and metadata creation have traditionally been the backbone of library operations. These processes ensure that information resources are organized, discoverable, and accessible.
But they are also time-consuming, labor-intensive, and prone to human error—especially as libraries now manage books, articles, digital repositories, multimedia, datasets, and institutional outputs.

Enter Artificial Intelligence.

AI-powered automation is redefining how libraries create, enrich, and maintain metadata. From auto-classification to entity extraction, AI is accelerating workflows and enabling librarians to focus on higher-level knowledge structuring.

 

1. How AI Is Transforming Cataloguing Workflows

1.1 Automated Metadata Extraction

AI tools can read a document’s:

• Title
• Abstract
• Keywords
• References
• Section headings

…and automatically extract metadata fields.

This is especially powerful for:

• ETDs
• Institutional repositories
• Grey literature
• Research datasets
• Digitized archives

AI saves hours of manual work.

 

1.2 AI-Assisted Classification & Subject Indexing

Tools using NLP and machine learning can:

• Suggest Dewey Decimal numbers
• Propose LCSH subject headings
• Detect themes and concepts
• Automatically tag resources

Examples:

• OCLC’s AI models
• AutoCat systems
• Generative AI-based classification assistants

These tools improve accuracy and consistency.

 

1.3 Entity Recognition & Linked Data

AI can identify:

• Authors
• Organizations
• Topics
• Dates
• Locations
• Research methods

and link them to authoritative identifiers such as:

• VIAF
• Wikidata
• ORCID

This brings libraries closer to a semantic web–ready metadata ecosystem.

 

2. Enhancing Discovery Through AI-Enriched Metadata

2.1 Semantic Search & Knowledge Graphs

AI can upgrade OPACs from keyword-based searching to meaning-based discovery, connecting:

• Concepts
• Authors
• Related works
• Citations
• Topics

2.2 Automatic Relationship Mapping

AI links resources automatically by:

• Subject similarity
• Co-authorship
• Citation networks
• Research themes

This strengthens discovery layers dramatically.

 

3. Benefits of AI in Technical Services

✔ Faster Processing

Metadata creation that once took days now takes minutes.

✔ Reduced Human Error

AI maintains consistency in classification and headings.

✔ Better User Discovery

Improved metadata → more accurate search results.

✔ Cost-Efficient Workflows

Less time on repetitive cataloguing → more time for digital curation.

✔ Supports Large Digital Collections

Especially institutional repositories growing exponentially.

 

4. Limitations and Ethical Issues

• Metadata Bias

If AI is trained on biased data, it may replicate outdated terminology or classifications.

• Lack of Transparency

AI suggestions must be reviewable and explainable

• Over-Automation Risks

Human cataloguers must validate all AI-generated metadata.

• Skills Gap

Librarians need AI literacy to evaluate algorithmic outputs.

 

5. The Changing Role of Technical Librarians

AI does not eliminate cataloguers—it elevates them into new roles:

• Metadata quality specialists
• Digital knowledge organizers
• Linked data architects
• AI workflow supervisors
• Ethical AI evaluators

The future of cataloguing is AI-assisted, librarian-guided.

 

Conclusion

AI is not just speeding up cataloguing—it is fundamentally reinventing how libraries create, manage, and connect metadata. With automated extraction, semantic enrichment, and intelligent classification, technical services are entering a new era where accuracy, speed, and user discovery are enhanced like never before.