A  main  feature  of  these  systems,  summarised  in  Table 4,  is  that  the  structured  knowledge  to  generate  explanations 
is  integrated  a  posteriori,  i.e.  once  obtained  the  outputs  of  the  models.  With  a  few  exceptions,  these  works  are  limited 
in  what  they  rely  on  the  manual  selection  of  the  knowledge  graphs,  requiring  an  expert  to  extract  from  these  useful 
background  knowledge  in  the  form  of  statements.  When  relying  reusing  existing  knowledge  graphs  to  avoid  the  time-
consuming knowledge acquisition steps, their performance is highly dependent on the freshness of the information stated 
in  ABox  assertions,  by  nature  more  dynamic  and  subject  to  expiration  over  time.  The  use  of  domain  knowledge  graphs 
which turned with the use of factual knowledge afterwards, alongside with explanations becoming more visual and fact-
Knowledge graphs as tools for explainable machine learning: A survey