  Knowledge graphs are used to represent relational information in terms of
triples. To enable learning about domains, embedding models, such as tensor
factorization models, can be used to make predictions of new triples. Often
there is background taxonomic information (in terms of subclasses and
subproperties) that should also be taken into account. We show that existing
fully expressive (a.k.a. universal) models cannot provably respect subclass and
subproperty information. We show that minimal modifications to an existing
knowledge graph completion method enables injection of taxonomic information.
Moreover, we prove that our model is fully expressive, assuming a lower-bound
on the size of the embeddings. Experimental results on public knowledge graphs
show that despite its simplicity our approach is surprisingly effective.
