  This paper tackles the problem of the semantic gap between a document and a
query within an ad-hoc information retrieval task. In this context, knowledge
bases (KBs) have already been acknowledged as valuable means since they allow
the representation of explicit relations between entities. However, they do not
necessarily represent implicit relations that could be hidden in a corpora.
This latter issue is tackled by recent works dealing with deep representation
learn ing of texts. With this in mind, we argue that embedding KBs within deep
neural architectures supporting documentquery matching would give rise to
fine-grained latent representations of both words and their semantic relations.
In this paper, we review the main approaches of neural-based document ranking
as well as those approaches for latent representation of entities and relations
via KBs. We then propose some avenues to incorporate KBs in deep neural
approaches for document ranking. More particularly, this paper advocates that
KBs can be used either to support enhanced latent representations of queries
and documents based on both distributional and relational semantics or to serve
as a semantic translator between their latent distributional representations.
