  Scene graph generation from images is a task of great interest to
applications such as robotics, because graphs are the main way to represent
knowledge about the world and regulate human-robot interactions in tasks such
as Visual Question Answering (VQA). Unfortunately, its corresponding area of
machine learning is still relatively in its infancy, and the solutions
currently offered do not specialize well in concrete usage scenarios.
Specifically, they do not take existing "expert" knowledge about the domain
world into account; and that might indeed be necessary in order to provide the
level of reliability demanded by the use case scenarios. In this paper, we
propose an initial approximation to a framework called Ontology-Guided Scene
Graph Generation (OG-SGG), that can improve the performance of an existing
machine learning based scene graph generator using prior knowledge supplied in
the form of an ontology (specifically, using the axioms defined within); and we
present results evaluated on a specific scenario founded in telepresence
robotics. These results show quantitative and qualitative improvements in the
generated scene graphs.
