System and method for out-of-sample representation learning

Abstract

Disclosed are systems, methods, and devices for out-of-sample representation learning using knowledge graphs. An embedding data structure reflective of a knowledge graph embedding model is received. A training data set including a plurality of training data entries, each of the training data entries reflective of a head entity, a tail entity, and a relation therebetween, wherein at least one of the head entities or the tail entities includes an out-of-sample entity, is received. A plurality of knowledge graph embedding model processors is provided. A random number is generated and compared to at least one criterion. A knowledge graph embedding model processor is selected from among the plurality of knowledge graph embedding model processors based at least in part on the comparing. The embedding data structure is processed with the selected knowledge graph embedding model processor.