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

Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dat Quoc Nguyen, Dinh Phung
8/13/2018
cs.CLcs.IR

Official code: daiquocnguyen/CapsE (147 stars, Apache-2.0, last commit 2021-04-14).

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.

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Code Implementations(1)

daiquocnguyen/CapsEOfficial100%
Apache-2.0

A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization (NAACL 2019)

14732Shell, PythonNov 3, 20185 years agoApache-2.0
capsule-networkcapsule-neural-networksknowledge-baseknowledge-base-completionknowledge-base-embeddings+5 more

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Cite this paper

@article{nguyen2018capsule,
  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},
  year   = {2018},
  eprint = {1808.04122},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1808.04122}
}

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