ad-hoc, automatically-built graph of entities and user behaviours, and a set of minimal properties (e.g. produced by, category, 
also viewed)  is  embedded  in  the  model.  The  generated  personalised  recommendations  and  their  explanations  expressed 
in  natural  language  are  then  built  using  a  soft-matching  algorithm  over  the  knowledge  graph.  Along  the  same  lines  but 
reusing  existing  knowledge  sources,  authors  of [68] propose  to  substitute  the  hidden  layers  and  the  connections  of  an 
autoencoder neural network with the structure of DBpedia, thus following the principles of the Semantics-Aware Autoen-
coders [69]. To deﬁne movies, authors use a small set of predicates (dct:subject,dct:starring,  dct:director, 
dct:writer), and then formulate human-understandable explanations by relying on the weights associated to features in
Knowledge graphs as tools for explainable machine learning: A survey