@inproceedings{fu-etal-2022-tempcaps,
title = "{T}emp{C}aps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion",
author = "Fu, Guirong and
Meng, Zhao and
Han, Zhen and
Ding, Zifeng and
Ma, Yunpu and
Schubert, Matthias and
Tresp, Volker and
Wattenhofer, Roger",
editor = "Vlachos, Andreas and
Agrawal, Priyanka and
Martins, Andr{\'e} and
Lampouras, Gerasimos and
Lyu, Chunchuan",
booktitle = "Proceedings of the Sixth Workshop on Structured Prediction for NLP",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.spnlp-1.3",
doi = "10.18653/v1/2022.spnlp-1.3",
pages = "22--31",
abstract = "Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, we tackle the temporal knowledge graph completion task by proposing \textbf{TempCaps}, which is a \textbf{Caps}ule network-based embedding model for \textbf{Temp}oral knowledge graph completion. TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks. Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information. Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion. Additional analysis also shows that TempCaps is efficient.",
}
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<abstract>Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion. TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks. Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information. Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion. Additional analysis also shows that TempCaps is efficient.</abstract>
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%0 Conference Proceedings
%T TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion
%A Fu, Guirong
%A Meng, Zhao
%A Han, Zhen
%A Ding, Zifeng
%A Ma, Yunpu
%A Schubert, Matthias
%A Tresp, Volker
%A Wattenhofer, Roger
%Y Vlachos, Andreas
%Y Agrawal, Priyanka
%Y Martins, André
%Y Lampouras, Gerasimos
%Y Lyu, Chunchuan
%S Proceedings of the Sixth Workshop on Structured Prediction for NLP
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F fu-etal-2022-tempcaps
%X Temporal knowledge graphs store the dynamics of entities and relations during a time period. However, typical temporal knowledge graphs often suffer from incomplete dynamics with missing facts in real-world scenarios. Hence, modeling temporal knowledge graphs to complete the missing facts is important. In this paper, we tackle the temporal knowledge graph completion task by proposing TempCaps, which is a Capsule network-based embedding model for Temporal knowledge graph completion. TempCaps models temporal knowledge graphs by introducing a novel dynamic routing aggregator inspired by Capsule Networks. Specifically, TempCaps builds entity embeddings by dynamically routing retrieved temporal relation and neighbor information. Experimental results demonstrate that TempCaps reaches state-of-the-art performance for temporal knowledge graph completion. Additional analysis also shows that TempCaps is efficient.
%R 10.18653/v1/2022.spnlp-1.3
%U https://aclanthology.org/2022.spnlp-1.3
%U https://doi.org/10.18653/v1/2022.spnlp-1.3
%P 22-31
Markdown (Informal)
[TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion](https://aclanthology.org/2022.spnlp-1.3) (Fu et al., spnlp 2022)
ACL
- Guirong Fu, Zhao Meng, Zhen Han, Zifeng Ding, Yunpu Ma, Matthias Schubert, Volker Tresp, and Roger Wattenhofer. 2022. TempCaps: A Capsule Network-based Embedding Model for Temporal Knowledge Graph Completion. In Proceedings of the Sixth Workshop on Structured Prediction for NLP, pages 22–31, Dublin, Ireland. Association for Computational Linguistics.