An energy-based model for neuro-symbolic reasoning on knowledge graphs

TL;DR

This paper introduces an energy-based graph embedding model for neuro-symbolic reasoning on knowledge graphs, enabling context-aware predictions and anomaly evaluation in industrial systems, with potential applications in neuromorphic computing.

Contribution

It presents a novel energy-based graph embedding algorithm that integrates multi-domain knowledge for improved reasoning and anomaly detection in industrial automation systems.

Findings

Model can predict system events contextually.

Effective in evaluating anomaly severity.

Bridges graph embedding with neuromorphic architectures.

Abstract

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.