Automated metadata annotation: What is and is not possible with machine learning

Ming Fang Wu, Hans Brandhorst, Maria Cristina Marinescu, Joaquim More Lopez, Margorie Hlava, Joseph Busch

Producción científica: Artículo en revista indizadaArtículorevisión exhaustiva

6 Citas (Scopus)

Resumen

Automated metadata annotation is only as good as training dataset, or rules that are available for the domain. It’s important to learn what type of data content a pre-trained machine learning algorithm has been trained on to understand its limitations and potential biases. Consider what type of content is readily available to train an algorithm—what’s popular and what’s available. However, scholarly and historical content is often not available in consumable, homogenized, and interoperable formats at the large volume that is required for machine learning. There are exceptions such as science and medicine, where large, well documented collections are available. This paper presents the current state of automated metadata annotation in cultural heritage and research data, discusses challenges identified from use cases, and proposes solutions.

Idioma originalInglés
Páginas (desde-hasta)122-138
Número de páginas17
PublicaciónData Intelligence
Volumen5
N.º1
DOI
EstadoPublicada - 1 dic 2023
Publicado de forma externa

Huella

Profundice en los temas de investigación de 'Automated metadata annotation: What is and is not possible with machine learning'. En conjunto forman una huella única.

Citar esto