  In Human-Robot Interaction (HRI) systems, a challenging task is sharing the
representation of the operational environment, fusing symbolic knowledge and
perceptions, between users and robots. With the existing HRI pipelines, users
can teach the robots some concepts to increase their knowledge base.
Unfortunately, the data coming from the users are usually not enough dense for
building a consistent representation. Furthermore, the existing approaches are
not able to incrementally build up their knowledge base, which is very
important when robots have to deal with dynamic contexts. To this end, we
propose an architecture to gather data from users and environments in long-runs
of continual learning. We adopt Knowledge Graph Embedding techniques to
generalize the acquired information with the goal of incrementally extending
the robot's inner representation of the environment. We evaluate the
performance of the overall continual learning architecture by measuring the
capabilities of the robot of learning entities and relations coming from
unknown contexts through a series of incremental learning sessions.
