  Embedding methods have demonstrated robust performance on the task of link
prediction in knowledge graphs, by mostly encoding entity relationships. Recent
methods propose to enhance the loss function with a literal-aware term. In this
paper, we propose KGA: a knowledge graph augmentation method that incorporates
literals in an embedding model without modifying its loss function. KGA
discretizes quantity and year values into bins, and chains these bins both
horizontally, modeling neighboring values, and vertically, modeling multiple
levels of granularity. KGA is scalable and can be used as a pre-processing step
for any existing knowledge graph embedding model. Experiments on legacy
benchmarks and a new large benchmark, DWD, show that augmenting the knowledge
graph with quantities and years is beneficial for predicting both entities and
numbers, as KGA outperforms the vanilla models and other relevant baselines.
Our ablation studies confirm that both quantities and years contribute to KGA's
performance, and that its performance depends on the discretization and binning
settings. We make the code, models, and the DWD benchmark publicly available to
facilitate reproducibility and future research.
