This is especially helpful in the case of commonsense KGs (e.g., ConceptNet (Speer et al., 2017)) that consist of non-canonicalized text and hence suffer from severe sparsity (Malaviya et al., 2019).
these methods usually feed concatenated triples (e.g., [HEAD, Relation, TAIL]) into LMs for training or finetuning.