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

• DOI: 10.48550/arxiv.2110.01639
• Publication Date: 2021-01-01
• Authors: Dominik Dold; Josep Soler Garrido
• Keywords: Knowledge graph; Neuromorphic engineering; Bridge (graph theory)

Machine learning on graph-structured data has recently become a major topic in industry and research, finding many exciting applications such as recommender systems automated theorem proving. We propose an energy-based graph embedding algorithm to characterize industrial automation systems, integrating knowledge from different domains like automation, communications cybersecurity. By combining multiple domains, the learned model is capable of making context-aware predictions regarding novel system events can be used evaluate severity anomalies that might indicative of, e.g., cybersecurity breaches. The presented mappable biologically-inspired neural architecture, serving first bridge between methods neuromorphic computing - uncovering promising edge application for this upcoming technology.