  Commonsense reasoning aims to incorporate sets of commonsense facts,
retrieved from Commonsense Knowledge Graphs (CKG), to draw conclusion about
ordinary situations. The dynamic nature of commonsense knowledge postulates
models capable of performing multi-hop reasoning over new situations. This
feature also results in having large-scale sparse Knowledge Graphs, where such
reasoning process is needed to predict relations between new events. However,
existing approaches in this area are limited by considering CKGs as a limited
set of facts, thus rendering them unfit for reasoning over new unseen
situations and events. In this paper, we present a neural-symbolic reasoner,
which is capable of reasoning over large-scale dynamic CKGs. The logic rules
for reasoning over CKGs are learned during training by our model. In addition
to providing interpretable explanation, the learned logic rules help to
generalise prediction to newly introduced events. Experimental results on the
task of link prediction on CKGs prove the effectiveness of our model by
outperforming the state-of-the-art models.
