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Knowledge graph (KG) embedding techniques use structured relationshipsbetween entities to learn low-dimensional representations of entities andrelations. The traditional KG embedding techniques (such as TransE andDistMult) estimate these embeddings via simple models developed over observedKG triplets. These approaches differ in their triplet scoring loss functions.As these models only use the observed triplets to estimate the embeddings, theyare prone to suffer through data sparsity that usually occurs in the real-worldknowledge graphs, i.e., the lack of enough triplets per entity. To settle thisissue, we propose an efficient method to augment the number of triplets toaddress the problem of data sparsity. We use random walks to create additionaltriplets, such that the relations carried by these introduced triplets entailthe metapath induced by the random walks. We also provide approaches toaccurately and efficiently filter out informative metapaths from the possibleset of metapaths, induced by the random walks. The proposed approaches aremodel-agnostic, and the augmented training dataset can be used with any KGembedding approach out of the box. Experimental results obtained on thebenchmark datasets show the advantages of the proposed approach.

Walk-and-Relate: A Random-Walk-based Algorithm for Representation Learning on Sparse Knowledge Graphs