These works, summarised in Table 5, present a number of novel features: the integration of knowledge graphs within 
the  model,  the  use  of  graphs  to  explain  (and  adjust)  how  the  model  reached  the  conclusions,  the  use  of  common-sense 
knowledge graphs rather than factual ones (likely due to common-sense knowledge being represented in images), and the 
use of a variety of different models for generating knowledge-based explanations, showing the ability to knowledge graphs 
to  generalise  across  tasks.  A  few  examples  are  given  in  Fig. 4,  where  we  can  see  how  semantic  restrictions  are  used  to 
explain  image  outputs  of  the  model.  We  note  that  the  approaches  are  mainly  focused  on  entailment  relationships  (i.e. 
subclasses),  limiting  the  potential  of  knowledge  graphs,  and  are  characterised  by  the  manual  step  of  extracting/aligning
Knowledge graphs as tools for explainable machine learning: A survey