The literature in explainable recommender systems here features aspects similar to the systems presented in the previous 
section, e.g. mostly models integrating knowledge graphs internally and explanations in written or visual form. Signiﬁcant 
differences lie mostly in the use of a large-scale, open-domain knowledge graph (DBpedia, mainly) to extract additional facts 
about the input data that can explain the recommendation in a user-friendly way, and in the lack of use of terminology 
axioms,  suggesting  the  diﬃculty  in  scaling  up  inference  techniques [73].  Open-domain  knowledge  graphs  also  introduce 
issues  such  as  information  overﬂow,  i.e.  the  high  outdegree  of  the  nodes  limits  the  selection  of  the  closest  facts  in  the 
graph,  often  shown  in  the  forms  of  multi-edge  paths  extracted  from  the  graphs  (cfr.  examples  of  Fig. 5).  This  not  only
Knowledge graphs as tools for explainable machine learning: A survey