Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus a machine-actionable GND taxonomy. The resource enables ontology-aware multi-label classification, mapping text to authority terms, and agent-assisted cataloging with reproducible, authority-grounded evaluation. We provide a brief statistical profile and qualitative error analyses of three systems. We invite the community to assess not only accuracy but usefulness and transparency, toward authority-anchored AI co-pilots that amplify catalogers' work.
@article{arxiv.2603.10876,
title = {An Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took "Use of Practical AI in Digital Libraries" seriously?},
author = {Jennifer D'Souza and Sameer Sadruddin and Maximilian Kähler and Andrea Salfinger and Luca Zaccagna and Francesca Incitti and Lauro Snidaro and Osma Suominen},
journal= {arXiv preprint arXiv:2603.10876},
year = {2026}
}
Comments
9 pages, 5 figures. Accepted to appear in the Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026)