We present a novel method for mapping unrestricted text to knowledge graph entities by framing the task as a sequence-to-sequence problem.
given the encoded state of an input text, our decoder directly predicts paths in the knowledge graph, starting from the root and ending at the target node following hypernym-hyponym relationships.