Knowledge-Based Entity Prediction for Improved Machine Perception in Autonomous Systems
Knowledge-Based Entity Prediction for Improved Machine Perception in Autonomous Systems
复制标题
DOI：
10.1109/mis.2022.3181015
复制
发表时间：
2022-03
影响因子：
6.4
通讯作者：
Ruwan Wickramarachchi;C. Henson;A. Sheth
中科院分区：
文献类型：
--
作者：
Ruwan Wickramarachchi;C. Henson;A. Sheth
基于知识的实体预测（KEP）是一个新的任务，旨在提高自主系统中的机器感知。KEP利用来自异构源的关系知识来预测可能无法识别的实体。在这篇文章中，我们提供了一个正式的定义，KEP作为一个知识完成任务。然后介绍了三种可能的解决方案，它们采用了几种机器学习和数据挖掘技术。最后，KEP的适用性证明了两个自主系统从不同的领域，即，自动驾驶和智能制造。我们认为，在复杂的现实世界系统中，KEP的使用将显着提高机器的感知能力，同时推动当前技术更接近实现完全自主。
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 article, 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.