Knowledge Association with Hyperbolic Knowledge Graph Embeddings

Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, Wei Zhang


Abstract
Capturing associations for knowledge graphs (KGs) through entity alignment, entity type inference and other related tasks benefits NLP applications with comprehensive knowledge representations. Recent related methods built on Euclidean embeddings are challenged by the hierarchical structures and different scales of KGs. They also depend on high embedding dimensions to realize enough expressiveness. Differently, we explore with low-dimensional hyperbolic embeddings for knowledge association. We propose a hyperbolic relational graph neural network for KG embedding and capture knowledge associations with a hyperbolic transformation. Extensive experiments on entity alignment and type inference demonstrate the effectiveness and efficiency of our method.
Anthology ID:
2020.emnlp-main.460
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5704–5716
Language:
URL:
https://aclanthology.org/2020.emnlp-main.460
DOI:
10.18653/v1/2020.emnlp-main.460
Bibkey:
Cite (ACL):
Zequn Sun, Muhao Chen, Wei Hu, Chengming Wang, Jian Dai, and Wei Zhang. 2020. Knowledge Association with Hyperbolic Knowledge Graph Embeddings. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5704–5716, Online. Association for Computational Linguistics.
Cite (Informal):
Knowledge Association with Hyperbolic Knowledge Graph Embeddings (Sun et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.460.pdf
Video:
 https://slideslive.com/38938826
Code
 nju-websoft/HyperKA
Data
DBP15K