Machine learning on graph-structured data has recently become a major topic in industry and research, finding many exciting applications such as recommender systems and automated theorem proving. We propose an energy-based graph embedding algorithm to characterize industrial automation systems, integrating knowledge from different domains like industrial automation, communications and cybersecurity. By combining knowledge from multiple domains, the learned model is capable of making context-aware predictions regarding novel system events and can be used to evaluate the severity of anomalies that might be indicative of, e.g., cybersecurity breaches. The presented model is mappable to a biologically-inspired neural architecture, serving as a first bridge between graph embedding methods and neuromorphic computing - uncovering a promising edge application for this upcoming technology.
@article{arxiv.2110.01639,
title = {An energy-based model for neuro-symbolic reasoning on knowledge graphs},
author = {Dominik Dold and Josep Soler Garrido},
journal= {arXiv preprint arXiv:2110.01639},
year = {2023}
}
Comments
Accepted for publication at the 20th IEEE International Conference on Machine Learning and Applications (ICMLA 2021)