  Knowledge Graph Embedding (KGE) is a common method for Knowledge Graphs (KGs)
to serve various artificial intelligence tasks. The suitable dimensions of the
embeddings depend on the storage and computing conditions of the specific
application scenarios. Once a new dimension is required, a new KGE model needs
to be trained from scratch, which greatly increases the training cost and
limits the efficiency and flexibility of KGE in serving various scenarios. In
this work, we propose a novel KGE training framework MED, through which we
could train once to get a croppable KGE model applicable to multiple scenarios
with different dimensional requirements, sub-models of the required dimensions
can be cropped out of it and used directly without any additional training. In
MED, we propose a mutual learning mechanism to improve the low-dimensional
sub-models performance and make the high-dimensional sub-models retain the
capacity that low-dimensional sub-models have, an evolutionary improvement
mechanism to promote the high-dimensional sub-models to master the knowledge
that the low-dimensional sub-models can not learn, and a dynamic loss weight to
balance the multiple losses adaptively. Experiments on 3 KGE models over 4
standard KG completion datasets, 3 real application scenarios over a real-world
large-scale KG, and the experiments of extending MED to the language model BERT
show the effectiveness, high efficiency, and flexible extensibility of MED.
