KBX-systems have exploited knowledge graphs to complement the limitations both of classical and modern (deep) Ma-
chine Learning approaches, e.g. need of large training data and inability to transfer the learning task, showing that a ﬂexible 
solution can support the development of end-to-end approaches generalising across tasks. Additionally, by encoding con-
cepts, relationships and their contexts, knowledge graphs offer an opportunity for systems to integrate inference and causal 
reasoning,  thus  improving  their  reactivity  and  decision-making  ability.  This  combination  is  very  well-known  by  neural-
symbolic approaches [97], aiming at combining the ability of Machine Learning approaches to learn from experience, and 
the one of Knowledge Representation frameworks to reason about what has been learnt. In this sense, knowledge graphs 
allow  to  develop  semantic-interoperable  solutions [98],  where  systems  exchange  and  interpret  information  produced  by
Knowledge graphs as tools for explainable machine learning: A survey