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

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 However, scholarly and historical content is often not available in consumable, homogenized, and interoperable formats at the large volume that is required for machine learning.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.Peer ReviewedPostprint (published version

Improving object detection in paintings based on time contexts

This paper proposes a novel approach to object detection for the Cultural Heritage domain, which relies on combining Deep Learning and semantic metadata about candidate objects extracted from existing sources such as Wikidata, dictionaries, or Google NGram. Working with cultural heritage presents challenges not present in every-day images. In computer vision, object detection models are usually trained with datasets whose classes are not imaginary concepts, and have neither symbolic nor time-specific dimensions. Apart from this conceptual problem, the paintings are limited in number and represent the same concept in potentially very different styles. Finally, the metadata associated with the images is often poor or inexistent, which makes it hard to properly train a model. Our approach can improve the precision of object detection by placing the classes detected by a neural network model in time, based on the dates of their first known use. By taking into account the time of inception of objects such as the TV, cell phone, or scissors, and the appearance of some objects in the geographical space that corresponds to a painting (e.g. bananas or broccoli in 15th century Europe), we can correct and refine the detected objects based on their chronologic probability.This research has been supported by the Saint George on a Bike project 2018-EU-IA-0104, co-financed by the Connecting Europe Facility of the European Union.Peer ReviewedPostprint (author's final draft