  Knowledge graph (KG) embedding techniques use structured relationships
between entities to learn low-dimensional representations of entities and
relations. The traditional KG embedding techniques (such as TransE and
DistMult) estimate these embeddings via simple models developed over observed
KG triplets. These approaches differ in their triplet scoring loss functions.
As these models only use the observed triplets to estimate the embeddings, they
are prone to suffer through data sparsity that usually occurs in the real-world
knowledge graphs, i.e., the lack of enough triplets per entity. To settle this
issue, we propose an efficient method to augment the number of triplets to
address the problem of data sparsity. We use random walks to create additional
triplets, such that the relations carried by these introduced triplets entail
the metapath induced by the random walks. We also provide approaches to
accurately and efficiently filter out informative metapaths from the possible
set of metapaths, induced by the random walks. The proposed approaches are
model-agnostic, and the augmented training dataset can be used with any KG
embedding approach out of the box. Experimental results obtained on the
benchmark datasets show the advantages of the proposed approach.
