  Knowledge graph embedding (KGE) that maps entities and relations into vector
representations is essential for downstream applications. Conventional KGE
methods require high-dimensional representations to learn the complex structure
of knowledge graph, but lead to oversized model parameters. Recent advances
reduce parameters by low-dimensional entity representations, while developing
techniques (e.g., knowledge distillation or reinvented representation forms) to
compensate for reduced dimension. However, such operations introduce
complicated computations and model designs that may not benefit large knowledge
graphs. To seek a simple strategy to improve the parameter efficiency of
conventional KGE models, we take inspiration from that deeper neural networks
require exponentially fewer parameters to achieve expressiveness comparable to
wider networks for compositional structures. We view all entity representations
as a single-layer embedding network, and conventional KGE methods that adopt
high-dimensional entity representations equal widening the embedding network to
gain expressiveness. To achieve parameter efficiency, we instead propose a
deeper embedding network for entity representations, i.e., a narrow entity
embedding layer plus a multi-layer dimension lifting network (LiftNet).
Experiments on three public datasets show that by integrating LiftNet, four
conventional KGE methods with 16-dimensional representations achieve comparable
link prediction accuracy as original models that adopt 512-dimensional
representations, saving 68.4% to 96.9% parameters.
