@inproceedings{yue-etal-2023-relation,
title = "Relation-aware Ensemble Learning for Knowledge Graph Embedding",
author = "Yue, Ling and
Zhang, Yongqi and
Yao, Quanming and
Li, Yong and
Wu, Xian and
Zhang, Ziheng and
Lin, Zhenxi and
Zheng, Yefeng",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.1034",
doi = "10.18653/v1/2023.emnlp-main.1034",
pages = "16620--16631",
abstract = "Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.",
}
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<abstract>Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.</abstract>
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%0 Conference Proceedings
%T Relation-aware Ensemble Learning for Knowledge Graph Embedding
%A Yue, Ling
%A Zhang, Yongqi
%A Yao, Quanming
%A Li, Yong
%A Wu, Xian
%A Zhang, Ziheng
%A Lin, Zhenxi
%A Zheng, Yefeng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yue-etal-2023-relation
%X Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.
%R 10.18653/v1/2023.emnlp-main.1034
%U https://aclanthology.org/2023.emnlp-main.1034
%U https://doi.org/10.18653/v1/2023.emnlp-main.1034
%P 16620-16631
Markdown (Informal)
[Relation-aware Ensemble Learning for Knowledge Graph Embedding](https://aclanthology.org/2023.emnlp-main.1034) (Yue et al., EMNLP 2023)
ACL
- Ling Yue, Yongqi Zhang, Quanming Yao, Yong Li, Xian Wu, Ziheng Zhang, Zhenxi Lin, and Yefeng Zheng. 2023. Relation-aware Ensemble Learning for Knowledge Graph Embedding. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16620–16631, Singapore. Association for Computational Linguistics.