  This paper studies the problem of predicting missing relationships between
entities in knowledge graphs through learning their representations. Currently,
the majority of existing link prediction models employ simple but intuitive
scoring functions and relatively small embedding size so that they could be
applied to large-scale knowledge graphs. However, these properties also
restrict the ability to learn more expressive and robust features. Therefore,
diverging from most of the prior works which focus on designing new objective
functions, we propose, DeCom, a simple but effective mechanism to boost the
performance of existing link predictors such as DistMult, ComplEx, etc, through
extracting more expressive features while preventing overfitting by adding just
a few extra parameters. Specifically, embeddings of entities and relationships
are first decompressed to a more expressive and robust space by decompressing
functions, then knowledge graph embedding models are trained in this new
feature space. Experimental results on several benchmark knowledge graphs and
advanced link prediction systems demonstrate the generalization and
effectiveness of our method. Especially, RESCAL + DeCom achieves
state-of-the-art performance on the FB15k-237 benchmark across all evaluation
metrics. In addition, we also show that compared with DeCom, explicitly
increasing the embedding size significantly increase the number of parameters
but could not achieve promising performance improvement.
