of  the  information  in  the  graph. [63] exploits  a  custom  knowledge  graph  automatically  built  from  images  and  existing 
meta-data sources integrated in a Markov Logic Network for zero-shot affordance prediction and object recognition avoiding 
the training of separate classiﬁers (for object labelling, attribute recognition, affordance detection). A Graph Convolutional 
Network that integrates both word embeddings and explicit knowledge from NELL to learn visual classiﬁcation of unseen 
classes (zero-shot learning) is used by [64]. The idea of integrating both word-level knowledge and common-sense semantics 
expressed in knowledge graphs within deep network structures is also investigated by [65], that exploits WordNet to create
Knowledge graphs as tools for explainable machine learning: A survey