TY - GEN
T1 - Semantic Deep Mapping in the Amsterdam Time Machine: Viewing Late 19th- and Early 20th-Century Theatre and Cinema Culture Through the Lens of Language Use and Socio-Economic Status
AU - Noordegraaf, Julia
AU - Van Erp, Marieke
AU - Zijdeman, Richard
AU - Raat, Mark
AU - Van Oort, Thunnis
AU - Zandhuis, Ivo
AU - Vermaut, Thomas
AU - Mol, Hans
AU - Van der Sijs, Nicoline
AU - Doreleijers, Kristel
AU - Baptist, Vincent
AU - Vrielink, Charlotte
AU - Assendelft, Brenda
AU - Rasterhoff, Claartje
AU - Kisjes, Ivan
N1 - Funding Information:
Acknowledgments. The research for this article, conducted in the context of the CLARIAH Amsterdam Time Machine project (2018–2019), was a collaboration between Fryske Akademy (Hans Mol, Mark Raat and Thomas Vermaut), KNAW Humanities Cluster (Gertjan Filarski, Marieke van Erp, Astrid Kulsdom), AdamNet (Henk Wals, Ivo Zandhuis), International Institute of Social History (Richard Zijde-man), Meertens Institute (Nicoline van der Sijs, Kristel Doreleijers, Brenda Assendelft) and University of Amsterdam (Julia Noordegraaf, Claartje Rasterhoff, Thunnis van Oort, Charlotte Vrielink and Vincent Baptist), and was financially supported by the NWO Roadmap for Large-scale Research Infrastructures project CLARIAH. Creating Linked Data for streets and districts and for cultural heritage collections in Amsterdam was done by the AdamNet Foundation in the AdamLink project, financed by the Pica Foundation (Stichting Pica).
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - In this paper, we present our work on semantic deep mapping at scale by combining information from various sources and disciplines to study historical Amsterdam. We model our data according to semantic web standards and ground them in space and time such that we can investigate what happened at a particular time and place from a linguistics, socio-economic and urban historical perspective. In a small use case we test the spatio-temporal infrastructure for research on entertainment culture in Amsterdam around the turn of the 20th century. We explain the bottlenecks we encountered for integrating information from different disciplines and sources and how we resolved or worked around them. Finally, we present a set of recommendations and best practices for adapting semantic deep mapping to other settings.
AB - In this paper, we present our work on semantic deep mapping at scale by combining information from various sources and disciplines to study historical Amsterdam. We model our data according to semantic web standards and ground them in space and time such that we can investigate what happened at a particular time and place from a linguistics, socio-economic and urban historical perspective. In a small use case we test the spatio-temporal infrastructure for research on entertainment culture in Amsterdam around the turn of the 20th century. We explain the bottlenecks we encountered for integrating information from different disciplines and sources and how we resolved or worked around them. Finally, we present a set of recommendations and best practices for adapting semantic deep mapping to other settings.
UR - https://link.springer.com/chapter/10.1007/978-3-030-93186-5_9
UR - http://www.scopus.com/inward/record.url?scp=85110410806&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-93186-5_9
DO - 10.1007/978-3-030-93186-5_9
M3 - Conference proceeding
SN - 9783030931858
SP - 191
EP - 212
BT - Research and Education in Urban History in the Age of Digital Libraries
A2 - Niebling, Florian
A2 - Münster, Sander
A2 - Messemer, Heike
ER -