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

TL;DR

This paper presents CapsE, a capsule network-based embedding model that improves knowledge graph completion and search personalization by modeling triples with capsules, achieving superior results on benchmark datasets.

Contribution

Introduces CapsE, a novel capsule network-based embedding model for knowledge graph completion and search personalization, outperforming existing models.

Findings

Outperforms state-of-the-art models on WN18RR and FB15k-237 datasets.

Achieves better results than strong baselines on SEARCH17.

Demonstrates the effectiveness of capsule networks in embedding tasks.

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.