AI in Digital Libraries & Repositories: Automation, Metadata & Access

  

By Niranjan Mohapatra, World Skill Center
Series: AI Transformation in Libraries (Part 8 of 10)

Digital libraries are rapidly evolving from static repositories into intelligent, automated knowledge ecosystems. Artificial Intelligence (AI) is now at the heart of this transformation, powering smarter metadata creation, automated ingestion, content enrichment, and enhanced access for users.

 

1. Automating Metadata Creation

One of the most time-consuming tasks in digital libraries is descriptive cataloging. AI tools now offer advanced support:

• Automatic metadata extraction from PDFs, images, videos, theses
• Entity recognition for author names, institutions, keywords
• Language identification and subject tagging
• Metadata standard mapping (Dublin Core, MARC21, MODS)

AI tools used: GROBID, spaCy, Trankit, OpenAI embeddings, machine-learning metadata extractors.

 

2. Intelligent Repository Workflows

AI helps automate key processes:

• Auto-classification into communities/collections
• Duplicate detection for preprints/postprints
• Version control for multiple uploads
• OCR enhancement for scanned documents
• PDF cleaning (rotation, de-skewing, de-noising)

These workflows reduce manual work and ensure consistency.

 

3. Enhancing Access & Discovery

AI-powered discovery layers provide:

• Semantic search (understanding concepts, not just keywords)
• Query expansion based on context
• Similarity-based recommendations (“Users who read this also read…”)
• Topic clustering & subject browsing
• Voice-based access & multilingual retrieval

This makes digital libraries more inclusive and user-friendly.

 

4. Improving Research Visibility

AI strengthens research impact by:

• Auto-generating altmetric insights
• Identifying collaboration trends
• Detecting research gaps from repository analytics
• Offering citation prediction
• Creating researcher profiles and knowledge graphs

 

5. Challenges & Ethical Issues

• Bias in AI-generated metadata
• Over-automation without librarian oversight
• Copyright concerns for text/data mining
• Privacy issues in usage analytics
• Need for transparency of algorithms

 

Conclusion

AI is not replacing digital librarianship — it is amplifying it. With proper governance, librarians can leverage AI to develop smarter, accessible, and scalable digital library systems for the future.