Relation Embedding with Dihedral Group in Knowledge Graph

Canran Xu, Ruijiang Li


Abstract
Link prediction is critical for the application of incomplete knowledge graph (KG) in the downstream tasks. As a family of effective approaches for link predictions, embedding methods try to learn low-rank representations for both entities and relations such that the bilinear form defined therein is a well-behaved scoring function. Despite of their successful performances, existing bilinear forms overlook the modeling of relation compositions, resulting in lacks of interpretability for reasoning on KG. To fulfill this gap, we propose a new model called DihEdral, named after dihedral symmetry group. This new model learns knowledge graph embeddings that can capture relation compositions by nature. Furthermore, our approach models the relation embeddings parametrized by discrete values, thereby decrease the solution space drastically. Our experiments show that DihEdral is able to capture all desired properties such as (skew-) symmetry, inversion and (non-) Abelian composition, and outperforms existing bilinear form based approach and is comparable to or better than deep learning models such as ConvE.
Anthology ID:
P19-1026
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
263–272
Language:
URL:
https://aclanthology.org/P19-1026
DOI:
10.18653/v1/P19-1026
Bibkey:
Cite (ACL):
Canran Xu and Ruijiang Li. 2019. Relation Embedding with Dihedral Group in Knowledge Graph. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 263–272, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Relation Embedding with Dihedral Group in Knowledge Graph (Xu & Li, ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1026.pdf
Video:
 https://vimeo.com/383993749
Data
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