TY - CHAP

T1 - Teaching Provenance to AI

T2 - An Annotation Scheme for Museum Data

AU - Mariani, Fabio

AU - Rother, Lynn

AU - Koss, Max

N1 - Publisher Copyright: © Sonja Thiel, Johannes C. Bernhardt (eds.). All rights reserved.

PY - 2023/12/27

Y1 - 2023/12/27

N2 - 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)

AB - 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)

KW - Science of art

UR - http://www.scopus.com/inward/record.url?scp=85182319765&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/951b9b07-7662-310f-8003-a06e7be1da2b/

U2 - 10.14361/9783839467107-014

DO - 10.14361/9783839467107-014

M3 - Contributions to collected editions/anthologies

SN - 978-3-8376-6710-3

SP - 163

EP - 172

BT - AI in Museums

A2 - Thiel, Sonja

A2 - Bernhardt, Johannes

PB - transcript Verlag

CY - Bielefeld

ER -