  Recent research efforts aiming to bridge the Neural-Symbolic gap for RDFS
reasoning proved empirically that deep learning techniques can be used to learn
RDFS inference rules. However, one of their main deficiencies compared to
rule-based reasoners is the lack of derivations for the inferred triples (i.e.
explainability in AI terms). In this paper, we build on these approaches to
provide not only the inferred graph but also explain how these triples were
inferred. In the graph words approach, RDF graphs are represented as a sequence
of graph words where inference can be achieved through neural machine
translation. To achieve explainability in RDFS reasoning, we revisit this
approach and introduce a new neural network model that gets the input graph--as
a sequence of graph words-- as well as the encoding of the inferred triple and
outputs the derivation for the inferred triple. We evaluated our justification
model on two datasets: a synthetic dataset-- LUBM benchmark-- and a real-world
dataset --ScholarlyData about conferences-- where the lowest validation
accuracy approached 96%.
