As  a  general  observation,  the  use  of  knowledge  graphs  to  generate  behavioural  explanations  as  an  a  posteriori  pro-
cess still requires investigation. Additionally, a shift from using common-sense knowledge graphs to more domain-speciﬁc 
knowledge graphs can be observed between systems generating model behaviour and data explanations. Similarly, mech-
anistic  explanations  tend  to  be  generated  using  membership  assertions  (ABox),  while  categorical  explanations  are  rather 
generated by exploiting terminological knowledge (TBox). Both trends can be explained by the size of knowledge graphs 
KBX-systems have to reason upon, i.e. the current KBX-systems rely on complex architectures that are less able to reason 
over very large knowledge graphs, when compared to systems for categorical explanations, that can reason upon smaller, 
domain-speciﬁc graphs, typically less computationally expensive.
Knowledge graphs as tools for explainable machine learning: A survey