  Knowledge-based entity prediction (KEP) is a novel task that aims to improve
machine perception in autonomous systems. KEP leverages relational knowledge
from heterogeneous sources in predicting potentially unrecognized entities. In
this paper, we provide a formal definition of KEP as a knowledge completion
task. Three potential solutions are then introduced, which employ several
machine learning and data mining techniques. Finally, the applicability of KEP
is demonstrated on two autonomous systems from different domains; namely,
autonomous driving and smart manufacturing. We argue that in complex real-world
systems, the use of KEP would significantly improve machine perception while
pushing the current technology one step closer to achieving full autonomy.
