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.

翻译：基于知识的实体预测(KEP)是一项新颖的任务,旨在改善自主系统中的机器认知。KEP利用来自不同来源的关系知识来预测可能不被承认的实体。在本文件中,我们正式将KEP定义为一项知识完成的任务。然后引入了三种可能的解决办法,采用了若干机械学习和数据挖掘技术。最后,KEP在两个不同领域的自主系统即自主驾驶和智能制造中表现出了适用性。我们认为,在复杂的现实世界系统中,使用KEP将大大改进机器认知,同时将目前的技术推向实现充分自主的一步。