Annif: DIY automated subject indexing using multiple algorithms

Authors

  • Osma Suominen National Library of Finland

DOI:

https://doi.org/10.18352/lq.10285

Keywords:

metadata, automated subject indexing, natural language processing, machine learning

Abstract

Manually indexing documents for subject-based access is a labour-intensive process. We propose using metadata gathered from bibliographic databases to train algorithms that assist librarians in that work. We have developed Annif, an open source tool and microservice for automated subject indexing. After training it with a subject vocabulary and existing metadata, Annif can be used to assign subject headings for new documents. We have tested Annif with different document collections including scientific papers, old scanned books and contemporary e-books, Q&A pairs from an “ask a librarian” service, Finnish Wikipedia, and the archives of a local newspaper. The results of analysing scientific papers and current books have been reassuring, while other types of documents have proved to be more challenging. The current version is based on a combination of existing natural language processing and machine learning tools. By combining multiple approaches and existing open source algorithms, Annif can build on the strengths of individual algorithms and adapt to different settings. With Annif, we expect to improve subject indexing and classification processes especially for electronic documents as well as collections that otherwise would not be indexed at all.

Author Biography

Osma Suominen, National Library of Finland

Information Systems Specialist

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Published

2019-07-29

How to Cite

Suominen, O. (2019). Annif: DIY automated subject indexing using multiple algorithms. LIBER Quarterly: The Journal of the Association of European Research Libraries, 29(1), 1–25. https://doi.org/10.18352/lq.10285

Issue

Section

Articles
Received 2019-05-22
Published 2019-07-29