To derive contextualized representations from textual features, the book title and blurb are concatenated and then fed through BERT The non-text features are generated in a separate preprocessing step. The metadata features are represented as a ten-dimensional vector (two dimensions for gender, see Section SECREF10). Author embedding vectors have a length of 200 (see Section SECREF22). In the next step, all three representations are concatenated and passed into a MLP with two layers, 1024 units each and ReLu activation function. 
 Question: How do they combine text representations with the knowledge graph embeddings?
all three representations are concatenated and passed into a MLP