A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization

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 matri...

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Bibliographic Details
Published in:arXiv.org 2019-03
Main Authors: Nguyen, Dai Quoc, Vu, Thanh, Tu Dinh Nguyen, Nguyen, Dat Quoc, Dinh Phung
Format: Article
Language:English
Subjects:
Convolution
Embedding
Feature maps
Mathematical analysis
Matrix algebra
Matrix methods
Online Access:Get full text
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Summary: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.
ISSN:2331-8422