An Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took "Use of Practical AI in Digital Libraries" seriously?

TL;DR

A large bilingual (English/German) corpus of catalog records annotated with the Integrated Authority File (GND), plus a machine-actionable GND taxonomy that enables ontology-aware multi-label classification, mapping text to authority terms, and agent-assisted cataloging with reproducible, authority-grounded evaluation.

Abstract

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.

Figures (5)

• Figure 1: Four example GND records in our internal JSON representation.
• Figure 2: nDCG@5 scores by the five record types.
• Figure 3: nDCG@5 scores by the two languages.
• Figure 4: nDCG@k scores, where k=5,10,15,20, for qualitative evaluation on 10 records per five domains. For this exercise, subject predictions were manually labeled Y and I by subject specialists at the library.
• Figure 5: Train-frequency distribution (binned) for the System 3 predictions, split into true positives and three false-negative subtypes.