An energy-based model for neuro-symbolic reasoning on knowledge graphs
This work addresses the problem of improving predictions and anomaly detection in industrial automation systems for applications like cybersecurity, though it appears incremental as it builds on existing graph embedding methods.
The authors tackled the problem of making context-aware predictions about novel system events and evaluating anomaly severity in industrial automation systems by proposing an energy-based graph embedding algorithm that integrates knowledge from multiple domains. The result is a model that can characterize industrial automation systems and is mappable to a biologically-inspired neural architecture, bridging graph embedding methods with neuromorphic computing.
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.