Improving Knowledge Graph Embedding Using Simple Constraints

Boyang Ding, Quan Wang, Bin Wang, Li Guo


Abstract
Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Early works performed this task via simple models developed over KG triples. Recent attempts focused on either designing more complicated triple scoring models, or incorporating extra information beyond triples. This paper, by contrast, investigates the potential of using very simple constraints to improve KG embedding. We examine non-negativity constraints on entity representations and approximate entailment constraints on relation representations. The former help to learn compact and interpretable representations for entities. The latter further encode regularities of logical entailment between relations into their distributed representations. These constraints impose prior beliefs upon the structure of the embedding space, without negative impacts on efficiency or scalability. Evaluation on WordNet, Freebase, and DBpedia shows that our approach is simple yet surprisingly effective, significantly and consistently outperforming competitive baselines. The constraints imposed indeed improve model interpretability, leading to a substantially increased structuring of the embedding space. Code and data are available at https://github.com/iieir-km/ComplEx-NNE_AER.
Anthology ID:
P18-1011
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–121
Language:
URL:
https://www.aclweb.org/anthology/P18-1011
DOI:
10.18653/v1/P18-1011
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-1011.pdf
Note:
 P18-1011.Notes.pdf
Software:
 P18-1011.Software.zip
Video:
 https://vimeo.com/285807879
Presentation:
 P18-1011.Presentation.pdf