  Automatic art analysis has seen an ever-increasing interest from the pattern
recognition and computer vision community. However, most of the current work is
mainly based solely on digitized artwork images, sometimes supplemented with
some metadata and textual comments. A knowledge graph that integrates a rich
body of information about artworks, artists, painting schools, etc., in a
unified structured framework can provide a valuable resource for more powerful
information retrieval and knowledge discovery tools in the artistic domain. To
this end, this paper presents ArtGraph: an artistic knowledge graph based on
WikiArt and DBpedia. The graph, implemented in Neo4j, already provides
knowledge discovery capabilities without having to train a learning system. In
addition, the embeddings extracted from the graph are used to inject
"contextual" knowledge into a deep learning model to improve the accuracy of
artwork attribute prediction tasks.
