@inproceedings{xu-etal-2020-seek,
title = "{SEEK}: Segmented Embedding of Knowledge Graphs",
author = "Xu, Wentao and
Zheng, Shun and
He, Liang and
Shao, Bin and
Yin, Jian and
Liu, Tie-Yan",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.358",
doi = "10.18653/v1/2020.acl-main.358",
pages = "3888--3897",
abstract = "In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at \url{https://github.com/Wentao-Xu/SEEK}.",
}
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<abstract>In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at https://github.com/Wentao-Xu/SEEK.</abstract>
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%0 Conference Proceedings
%T SEEK: Segmented Embedding of Knowledge Graphs
%A Xu, Wentao
%A Zheng, Shun
%A He, Liang
%A Shao, Bin
%A Yin, Jian
%A Liu, Tie-Yan
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F xu-etal-2020-seek
%X In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering. However, existing methods for knowledge graph embedding can not make a proper trade-off between the model complexity and the model expressiveness, which makes them still far from satisfactory. To mitigate this problem, we propose a lightweight modeling framework that can achieve highly competitive relational expressiveness without increasing the model complexity. Our framework focuses on the design of scoring functions and highlights two critical characteristics: 1) facilitating sufficient feature interactions; 2) preserving both symmetry and antisymmetry properties of relations. It is noteworthy that owing to the general and elegant design of scoring functions, our framework can incorporate many famous existing methods as special cases. Moreover, extensive experiments on public benchmarks demonstrate the efficiency and effectiveness of our framework. Source codes and data can be found at https://github.com/Wentao-Xu/SEEK.
%R 10.18653/v1/2020.acl-main.358
%U https://aclanthology.org/2020.acl-main.358
%U https://doi.org/10.18653/v1/2020.acl-main.358
%P 3888-3897
Markdown (Informal)
[SEEK: Segmented Embedding of Knowledge Graphs](https://aclanthology.org/2020.acl-main.358) (Xu et al., ACL 2020)
ACL
- Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, and Tie-Yan Liu. 2020. SEEK: Segmented Embedding of Knowledge Graphs. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3888–3897, Online. Association for Computational Linguistics.