=Paper=
{{Paper
|id=Vol-2949/invited1
|storemode=property
|title=Capturing Complex Heritage Knowledge - Abstract (invited paper)
|pdfUrl=https://ceur-ws.org/Vol-2949/invited1.pdf
|volume=Vol-2949
|authors=Marieke van Erp
|dblpUrl=https://dblp.org/rec/conf/swodch/Erp21
}}
==Capturing Complex Heritage Knowledge - Abstract (invited paper)==
<pdf width="1500px">https://ceur-ws.org/Vol-2949/invited1.pdf</pdf>
<pre>
     Capturing Complex Heritage Knowledge -
                   Abstract

                                 Marieke van Erp

                           KNAW Humanities Cluster
                           Amsterdam, the Netherlands
                        marieke.van.erp@dh.huc.knaw.nl



Abstract
Big data is not only complex to work with because there may be a lot of data
points, but also because each data point can be multifaceted and thus complex
to model and query. This is especially true for cultural heritage datasets that
deal with objects whose meaning has changed over time and whose position
in heritage collections has a complex relationship with the real world. In this
talk, I will discuss how the Digital Humanities Research Lab is developing and
working with advanced data models for cultural heritage collections in various
interdisciplinary collaborations.


Short Bio

Marieke van Erp is a Language Technology and Semantic Web expert with a pen-
chant for interdisciplinary research. She holds a PhD in computational linguistics
from Tilburg University (2010). She leads the Digital Humanities Research Lab
at KNAW Humanities Cluster (Amsterdam) and is a co-founder and scientific
co-director of the Cultural AI Lab, a collaboration between various heritage
and research institutes in the Netherlands. The Cultural AI Lab is aimed at
developing socio-technological AI systems that are implicitly or explicitly aware
of the subtle and subjective complexity of human culture. Dr. Van Erp’s work
spans many different domains, from olfactory, socio-economic, and maritime his-
tory to data journalism and cultural heritage but is always driven by language
understanding and knowledge representation.




Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).

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