Teaching Provenance to AI: An Annotation Scheme for Museum Data

Research output: Contributions to collected editions/worksContributions to collected editions/anthologiesResearch

Authors

Our paper addresses how artificial intelligence technologies can transform museum records of provenance into structured and machine-readable data, which is the first critical step in undertaking a large-scale cross-institutional analysis of object history. Drawing on research on natural language processing (NLP), we have identified sentence boundary disambiguation and span categorization as highly effective techniques for extracting and structuring information from provenance texts. Our paper focuses on a provenance-specific annotation scheme that enables us to retain historical nuances when constructing provenance linked open data (PLOD)
Original languageEnglish
Title of host publicationAI in Museums : Reflections, Perspectives and Applications
EditorsSonja Thiel, Johannes Bernhardt
Number of pages10
Place of PublicationBielefeld
Publishertranscript Verlag
Publication date27.12.2023
Pages163-172
ISBN (print)978-3-8376-6710-3
ISBN (electronic)978-3-8394-6710-7
DOIs
Publication statusPublished - 27.12.2023

Bibliographical note

Publisher Copyright:
© Sonja Thiel, Johannes C. Bernhardt (eds.). All rights reserved.

Documents

DOI