Holographic Embeddings of Knowledge Graphs
Name
CBMM-Memo-039.pdf
Size
677.87 KB
Format
Adobe PDF
Checksum (MD5)
d687807fb3eb26e8d53f22aee2f4cd9f
Author(s)
Nickel, Maximilian
Rosasco, Lorenzo
Poggio, Tomaso
Date Issued
November 16, 2015
Publisher
Center for Brains, Minds and Machines (CBMM), arXiv
Citation
arXiv:1510.04935
Series/Report no.
CBMM Memo Series;039
Abstract
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. In extensive experiments we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction in knowledge graphs and relational learning benchmark datasets.
Subjects
Associative Memory
Knowledge Graph
Machine Learning
Terms of Use
Attribution-NonCommercial 3.0 United States
Persistent DSpace Link
