  Knowledge Graph Embeddings (KGE) have become a quite popular class of models
specifically devised to deal with ontologies and graph structure data, as they
can implicitly encode statistical dependencies between entities and relations
in a latent space. KGE techniques are particularly effective for the biomedical
domain, where it is quite common to deal with large knowledge graphs underlying
complex interactions between biological and chemical objects. Recently in the
literature, the PharmKG dataset has been proposed as one of the most
challenging knowledge graph biomedical benchmark, with hundreds of thousands of
relational facts between genes, diseases and chemicals. Despite KGEs can scale
to very large relational domains, they generally fail at representing more
complex relational dependencies between facts, like logic rules, which may be
fundamental in complex experimental settings. In this paper, we exploit logic
rules to enhance the embedding representations of KGEs on the PharmKG dataset.
To this end, we adopt Relational Reasoning Network (R2N), a recently proposed
neural-symbolic approach showing promising results on knowledge graph
completion tasks. An R2N uses the available logic rules to build a neural
architecture that reasons over KGE latent representations. In the experiments,
we show that our approach is able to significantly improve the current
state-of-the-art on the PharmKG dataset. Finally, we provide an ablation study
to experimentally compare the effect of alternative sets of rules according to
different selection criteria and varying the number of considered rules.
