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


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.
Anthology ID:
N19-1226
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2180–2189
Language:
URL:
https://aclanthology.org/N19-1226
DOI:
10.18653/v1/N19-1226
Bibkey:
Cite (ACL):
Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dat Quoc Nguyen, and Dinh Phung. 2019. A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2180–2189, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization (Nguyen et al., NAACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/N19-1226.pdf
Code
 daiquocnguyen/CapsE +  additional community code
Data
FB15kWN18WN18RR