Holographic Embeddings of Knowledge Graphs

Understand

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.

• In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs.
• The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations.
• By using correlation as the compositional operator HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets.