The works presented here are summarised in Table 7 and show that knowledge graphs do provide background knowledge 
for explanations for a variety of contexts such as images, text, speech. The examples in Fig. 6 further reveal a predominant 
usage of multiple, connected sources rather than single ones. However, one of the main limitations is the limited use of the 
information in knowledge graphs, mostly restricted to nodes and their closest neighbourhood (i.e. 1-hop relationships such 
as “a motorbike is smaller than a car”). This is likely due to the computational costs related to a more advanced inferencing 
mechanisms.

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I. Tiddi and S. Schlobach

Artiﬁcial Intelligence 302 (2022) 103627

Fig. 6. Knowledge-based explanations in natural language applications.

4.5.  Predictive and forecasting tasks
Knowledge graphs as tools for explainable machine learning: A survey