  The rise of digitization of cultural documents offers large-scale contents,
opening the road for development of AI systems in order to preserve, search,
and deliver cultural heritage. To organize such cultural content also means to
classify them, a task that is very familiar to modern computer science.
Contextual information is often the key to structure such real world data, and
we propose to use it in form of a knowledge graph. Such a knowledge graph,
combined with content analysis, enhances the notion of proximity between
artworks so it improves the performances in classification tasks. In this
paper, we propose a novel use of a knowledge graph, that is constructed on
annotated data and pseudo-labeled data. With label propagation, we boost
artwork classification by training a model using a graph convolutional network,
relying on the relationships between entities of the knowledge graph. Following
a transductive learning framework, our experiments show that relying on a
knowledge graph modeling the relations between labeled data and unlabeled data
allows to achieve state-of-the-art results on multiple classification tasks on
a dataset of paintings, and on a dataset of Buddha statues. Additionally, we
show state-of-the-art results for the difficult case of dealing with unbalanced
data, with the limitation of disregarding classes with extremely low degrees in
the knowledge graph.
