Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems
This work addresses the challenge of enhancing perception for autonomous systems, but it appears incremental as it builds on existing machine learning and data mining techniques without claiming major breakthroughs.
The paper tackles the problem of improving machine perception in autonomous systems by introducing knowledge-based entity prediction (KEP) as a knowledge completion task, and demonstrates its applicability in autonomous driving and smart manufacturing, though no concrete performance numbers are provided.
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.