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

Data generated this way are incredibly sparse, i.e., only a Multi-relational knowledge graphs (KGs) [1] are rich data tiny fraction of possible triples are observed or even valid, structures used to model a variety of systems like industrial as well as streaming in nature such that triples can appear projects [2] and mathematical proofs [3]. It is therefore not multiple times and underlie stochastic variations. Using graph surprising that the interest in machine learning algorithms embedding, we reformulate the anomaly detection task as capable of dealing with graph-structured data has increased a link prediction task: events in the automation system are lately [4]. This broad applicability of graphs becomes apparent equivalent to new edges appearing in its graph representation when summarizing them as lists of triple statements that can be evaluated using the learned embeddings. However, (node, edge, node), e.g., (M.Hamill, plays, L.Skywalker) and we found that standard graph embedding algorithms perform (L.Skywalker, appearsIn, StarWars) - with individual entries poorly on such industrial graphs, mainly because they expect being called subject, predicate and object.