A model of exploratory search for evaluating its systems &amp; applications

Palagi, Emilie; Giboin, Alain; Gandon, Fabien; Troncy, Raphaël

doi:10.1145/3286689.3286716

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2022

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“…One solution that can help alleviate this problem for a cultural heritage domain such as European silk fabrics is an Exploratory search engine, which helps users to explore a topic of interest [1]. They enable serendipitous discovery of items, and they are especially appropriate when these items come with rich structured metadata.…”

Section: Introduction 1motivationmentioning

confidence: 99%

Multimodal Metadata Assignment for Cultural Heritage Artifacts

Rei

Mladenić

Dorozynski

et al. 2022

Preprint

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We develop a multimodal classifier for the cultural heritage domain using a late fusion approach and introduce a novel dataset. The three modalities are Image, Text, and Tabular data. We based the image classifier on a ResNet convolutional neural network architecture and the text classifier on a multilingual transformer architecture (XML-Roberta). Both are trained as multitask classifiers and use the focal loss to handle class imbalance. Tabular data and late fusion are handled by Gradient Tree Boosting. We also show how we leveraged specific data models and taxonomy in a Knowledge Graph to create the dataset and to store classification results. All individual classifiers accurately predict missing propertiesin the digitized silk artifacts, with the multimodal approach providing the best results.

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Section: Introduction 1motivationmentioning

confidence: 99%