Holographic Embeddings of Knowledge Graphs

Abstract

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on rela-tional 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 associa-tive 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. Experimentally, we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction on knowledge graphs and relational learning benchmark datasets.

Extracted Key Phrases

4 Figures and Tables

Showing 1-10 of 41 extracted citations
01002002014201520162017
Citations per Year

112 Citations

Semantic Scholar estimates that this publication has received between 57 and 208 citations based on the available data.

See our FAQ for additional information.