Knowledge-Based Entity Prediction for Improved Machine Perception in Autonomous Systems
ORCID iD
Wickramarachchi: 0000-0001-5810-1849
Henson: 0000-0003-3875-3705
Sheth: 0000-0002-0021-5293
Document Type
Article
Abstract
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.
Digital Object Identifier (DOI)
Publication Info
Published in IEEE Intelligent Systems, Volume 37, Issue 5, 2022, pages 42-49.
© 2022 IEEE
APA Citation
Wickramarachchi, R., Henson, C., & Sheth, A. (2022). Knowledge-Based Entity Prediction for Improved Machine Perception in Autonomous Systems. IEEE Intelligent Systems, 37(5), 42–49. https://doi.org/10.1109/MIS.2022.3181015