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This article compares two multimodal resources that consist of diagrams whichdescribe topics in elementary school natural sciences. Both resources containthe same diagrams and represent their structure using graphs, but differ interms of their annotation schema and how the annotations have been created -depending on the resource in question - either by crowd-sourced workers ortrained experts. This article reports on two experiments that evaluate howeffectively crowd-sourced and expert-annotated graphs can represent themultimodal structure of diagrams for representation learning using variousgraph neural networks. The results show that the identity of diagram elementscan be learned from their layout features, while the expert annotations providebetter representations of diagram types.

Classifying Diagrams and Their Parts using Graph Neural Networks: A Comparison of Crowd-Sourced and Expert Annotations