@inproceedings{sun-etal-2020-knowledge,
title = "Knowledge Association with Hyperbolic Knowledge Graph Embeddings",
author = "Sun, Zequn and
Chen, Muhao and
Hu, Wei and
Wang, Chengming and
Dai, Jian and
Zhang, Wei",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.460",
doi = "10.18653/v1/2020.emnlp-main.460",
pages = "5704--5716",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Knowledge Association with Hyperbolic Knowledge Graph Embeddings
%A Sun, Zequn
%A Chen, Muhao
%A Hu, Wei
%A Wang, Chengming
%A Dai, Jian
%A Zhang, Wei
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sun-etal-2020-knowledge
%X 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.
%R 10.18653/v1/2020.emnlp-main.460
%U https://aclanthology.org/2020.emnlp-main.460
%U https://doi.org/10.18653/v1/2020.emnlp-main.460
%P 5704-5716
Markdown (Informal)
[Knowledge Association with Hyperbolic Knowledge Graph Embeddings](https://aclanthology.org/2020.emnlp-main.460) (Sun et al., EMNLP 2020)
ACL