@inproceedings{ying-etal-2024-simple,
title = "Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion",
author = "Ying, Rui and
Hu, Mengting and
Wu, Jianfeng and
Xie, Yalan and
Liu, Xiaoyi and
Wang, Zhunheng and
Jiang, Ming and
Gao, Hang and
Zhang, Linlin and
Cheng, Renhong",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.luhme-long.596/",
doi = "10.18653/v1/2024.acl-long.596",
pages = "11074--11086",
abstract = "Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs. Current approaches usually embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. However, these methods only adopt a single operation, which may have limitations in capturing the complex temporal dynamics present in temporal knowledge graphs. Therefore, we propose a simple but effective method, i.e. TCompoundE, which is specially designed with two geometric operations, including time-specific and relation-specific operations. We provide mathematical proofs to demonstrate the ability of TCompoundE to encode various relation patterns. Experimental results show that our proposed model significantly outperforms existing temporal knowledge graph embedding models. Our code is available at https://github.com/nk-ruiying/TCompoundE."
}
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<abstract>Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs. Current approaches usually embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. However, these methods only adopt a single operation, which may have limitations in capturing the complex temporal dynamics present in temporal knowledge graphs. Therefore, we propose a simple but effective method, i.e. TCompoundE, which is specially designed with two geometric operations, including time-specific and relation-specific operations. We provide mathematical proofs to demonstrate the ability of TCompoundE to encode various relation patterns. Experimental results show that our proposed model significantly outperforms existing temporal knowledge graph embedding models. Our code is available at https://github.com/nk-ruiying/TCompoundE.</abstract>
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%0 Conference Proceedings
%T Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion
%A Ying, Rui
%A Hu, Mengting
%A Wu, Jianfeng
%A Xie, Yalan
%A Liu, Xiaoyi
%A Wang, Zhunheng
%A Jiang, Ming
%A Gao, Hang
%A Zhang, Linlin
%A Cheng, Renhong
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F ying-etal-2024-simple
%X Temporal knowledge graph completion aims to infer the missing facts in temporal knowledge graphs. Current approaches usually embed factual knowledge into continuous vector space and apply geometric operations to learn potential patterns in temporal knowledge graphs. However, these methods only adopt a single operation, which may have limitations in capturing the complex temporal dynamics present in temporal knowledge graphs. Therefore, we propose a simple but effective method, i.e. TCompoundE, which is specially designed with two geometric operations, including time-specific and relation-specific operations. We provide mathematical proofs to demonstrate the ability of TCompoundE to encode various relation patterns. Experimental results show that our proposed model significantly outperforms existing temporal knowledge graph embedding models. Our code is available at https://github.com/nk-ruiying/TCompoundE.
%R 10.18653/v1/2024.acl-long.596
%U https://aclanthology.org/2024.luhme-long.596/
%U https://doi.org/10.18653/v1/2024.acl-long.596
%P 11074-11086
Markdown (Informal)
[Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion](https://aclanthology.org/2024.luhme-long.596/) (Ying et al., ACL 2024)
ACL
- Rui Ying, Mengting Hu, Jianfeng Wu, Yalan Xie, Xiaoyi Liu, Zhunheng Wang, Ming Jiang, Hang Gao, Linlin Zhang, and Renhong Cheng. 2024. Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11074–11086, Bangkok, Thailand. Association for Computational Linguistics.