  In this paper, we focus on the classification of books using short
descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a
deep neural language model, we demonstrate how to combine text representations
with metadata and knowledge graph embeddings, which encode author information.
Compared to the standard BERT approach we achieve considerably better results
for the classification task. For a more coarse-grained classification using
eight labels we achieve an F1- score of 87.20, while a detailed classification
using 343 labels yields an F1-score of 64.70. We make the source code and
trained models of our experiments publicly available
