  This paper presents sandra, a neuro-symbolic reasoner combining vectorial
representations with deductive reasoning. Sandra builds a vector space
constrained by an ontology and performs reasoning over it. The geometric nature
of the reasoner allows its combination with neural networks, bridging the gap
with symbolic knowledge representations. Sandra is based on the Description and
Situation (DnS) ontology design pattern, a formalization of frame semantics.
Given a set of facts (a situation) it allows to infer all possible perspectives
(descriptions) that can provide a plausible interpretation for it, even in
presence of incomplete information. We prove that our method is correct with
respect to the DnS model. We experiment with two different tasks and their
standard benchmarks, demonstrating that, without increasing complexity, sandra
(i) outperforms all the baselines (ii) provides interpretability in the
classification process, and (iii) allows control over the vector space, which
is designed a priori.
