# Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems

arXiv: https://arxiv.org/abs/2203.16616 · HTML: https://mlsanity.com/paper/2203.16616/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015)
- [A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network](https://mlsanity.com/paper/1712.02121/) (2017)
- [Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning](https://mlsanity.com/paper/1811.02307/) (2018)
- [CSKG: The CommonSense Knowledge Graph](https://mlsanity.com/paper/2012.11490/) (2020)
- [An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice](https://mlsanity.com/paper/2003.00344/) (2020)

## Built on by (most cited first, 2 of 2)

- [Enhancing Cross-Modal Contextual Congruence for Crowdfunding Success using Knowledge-infused Learning](https://mlsanity.com/paper/2402.03607/) (2024)
- [Towards Infusing Auxiliary Knowledge for Distracted Driver Detection](https://mlsanity.com/paper/2408.16621/) (2024)

## Abstract (arXiv)

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.