Ernest Shackle

Holographic Embeddings are an approach to generating entity embeddings from a list of (head, tail, relation) triples like (“Jeff”, “Amazon”, “employer”) and (“Zuck”, “Palo Alto”, “location”). Embeddings can be used as lossy, but memory-efficient inputs to other machine learning models, used directly in triple inference (aka Knowledge Base Completion or Link Prediction) by evaluating candidate triples, or to search for associated entities using k-nearest neighbors.

For example, a search from the embedding representing the entity “University of California, Berkeley” yields the associated entities UC Irvine, Stanford University, USC, UCLA, and UCSD.