RT Journal Article
T1 The potential and pitfalls of machine learning in the Congruence Engine context
A1 John Stack
A1 Jamie Unwin
YR 2023
VO Congruence Engine
IS Autumn 2022
K1 Artificial Intelligence
K1 digital humanities
K1 Machine Learning
K1 ML
K1 Museum collections
K1 natural language processing
K1 Wikidata
AB This article considers the role of machine learning (ML) in the Congruence Engine project. The authors bring their digital and ML experience to the project and here reflect on existing tools and approaches, the particular challenges of Congruence Engine endeavour, and possible solutions within and beyond the project. Although these ideas will develop as the project progresses, the authors draw on knowledge of the existing ML landscape and current digital collections practice as well as learnings from Heritage Connector, the Science Museum Group’s previous project in the same funding scheme as Congruence Engine. The authors propose that while significant advances in ML and the availability of open datasets such as Wikidata offer huge opportunities for linking heritage collections, this will require a pipeline model with iterative stages of human intervention. Closer relationships will need to be developed between human curators, researchers and users of ML and the technology and processes it requires and this article points to the likely areas of collaboration that Congruence Engine will explore and test.
NO https://artuk.org/  (accessed 28 October 2022)
NO https://www.europeana.eu/  (accessed 28 October 2022)
NO For a more detailed discussion of these challenges see Rhiannon Lewis and John Stack, ‘Sidestepping the Limitations of Collection Catalogues with Machine Learning and Wikidata’, Heritage Connector Blog, 23 September 2020 https://thesciencemuseum.github.io/heritageconnector/post/2020/09/23/sidestepping-the-limitations-of-collections-catalogues-with-machine-learning-and-wikidata/ (accessed 6 October 2022)
NO https://orcid.org/ (accessed 22 November 2022)
NO See https://thesciencemuseum.github.io/heritageconnector/post/2021/12/05/Hackathon-Demos/
PB The Science Museum Group
SN 2054-5770
LA eng
DO 10.15180/221816
UL https://journal.sciencemuseum.ac.uk/article/the-potential-and-pitfalls-of-machine-learning-in-the-congruence-engine-context/
WT Science Museum Group Journal
OL 30