  Current methods for embedding-based query answering over incomplete Knowledge
Graphs (KGs) only focus on inductive reasoning, i.e., predicting answers by
learning patterns from the data, and lack the complementary ability to do
deductive reasoning, which requires the application of domain knowledge to
infer further information. To address this shortcoming, we investigate the
problem of incorporating ontologies into embedding-based query answering models
by defining the task of embedding-based ontology-mediated query answering. We
propose various integration strategies into prominent representatives of
embedding models that involve (1) different ontology-driven data augmentation
techniques and (2) adaptation of the loss function to enforce the ontology
axioms. We design novel benchmarks for the considered task based on the LUBM
and the NELL KGs and evaluate our methods on them. The achieved improvements in
the setting that requires both inductive and deductive reasoning are from 20%
to 55% in HITS@3.
