In an extended Trepan [89] ontologies-based decision trees are used to explain the outcomes of a prediction model in 
the context of loan prediction. The user-based evaluation shows that the ontology plays a positive role not only in improving 
the performance of the model, but also in the perceived understandability of the decisions. A domain ontology is also used 
by [90], and integrated in a neuro-symbolic architecture with a Restricted Boltzman Machines model in order to explain 
human  behaviours  in  health  social  networks.  The  ontology  is  used  to  create  symbolic  representation  for  the  users  and 
predict their behaviours, while explanations are generated a posteriori step as a set of triples that maximise the likelihood 
of a user behaviour.
Knowledge graphs as tools for explainable machine learning: A survey