To this end, we propose a new model called DialKG Walker that can learn natural knowledge paths among entities mentioned over dialog contexts, and reason grounded on a large commonsense KG.
we propose a novel graph decoder that attends on viable KG paths to predict the most relevant entities from a KG, by associating these paths with the given input contexts: dialog, sentence, and a set of starting KG entities mentioned in the previous turn.