A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
Abstract
In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix where each column vector represents the embedding of an element in the triple. This 3-column matrix is then fed to a convolution layer where multiple filters are operated to generate different feature maps. These feature maps are reconstructed into corresponding capsules which are then routed to another capsule to produce a continuous vector. The length of this vector is used to measure the plausibility score of the triple. Our proposed CapsE obtains better performance than previous state-of-the-art embedding models for knowledge graph completion on two benchmark datasets WN18RR and FB15k-237, and outperforms strong search personalization baselines on SEARCH17.
Cite
@article{arxiv.1808.04122,
title = {A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization},
author = {Dai Quoc Nguyen and Thanh Vu and Tu Dinh Nguyen and Dat Quoc Nguyen and Dinh Phung},
journal= {arXiv preprint arXiv:1808.04122},
year = {2019}
}
Comments
To appear in Proceedings of NAACL 2019. 10 pages