A post-hoc approach suggesting that knowledge graphs can be used not only to generate human-understandable expla-
nations, but also to augment the model with additional knowledge on the task at hand is presented in [71] for generating 
natural language explanations to recommend movies using DBpedia on top of the ExpLOD framework. A ranking algorithm 
is proposed to rank the most relevant properties to items that a user likes, which are then used to build natural language 
explanations. Another interesting argument is brought forward by [72], arguing that explainable recommender systems are 
limited in what they only incorporate knowledge from the node’s closest neighbours, and lack of proper solutions to ﬁl-
ter  unrelated  entities.  The  authors  propose  to  explain  travel  recommendations  using  the  unstructured  data  of  items  that 
have rich textual content that can be valuable to build explanations (e.g. books, news, touristic tours...). DBpedia is used
Knowledge graphs as tools for explainable machine learning: A survey