et al. (54), the authors used a residual neural network (ResNet) in a Visual Question Answering (VQA) problem wherein
the model supported the answers using image and text explanations. A decision-tree-based model with a KG was also
used in Ko et al. (50) to capture the semantic relationships between knowledge elements for the purpose of designing
rules in the additive manufacturing domain. With respect to KG reasoning, the authors of Gaur et al. (45) leveraged
external knowledge to enable the embedding of KGs in hidden layers in order to generate explainable outcomes by tra-
cing over the KG. Finally, the authors of Yu et al. (24) also used NLP techniques on a tax KG to explain the calculated
results and tax refunds through reasoning.
Knowledge-graph-based explainable AI- A systematic review