@inproceedings{han-etal-2021-learning-neural,
title = "Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs",
author = "Han, Zhen and
Ding, Zifeng and
Ma, Yunpu and
Gu, Yujia and
Tresp, Volker",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.658",
doi = "10.18653/v1/2021.emnlp-main.658",
pages = "8352--8364",
abstract = "There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary differential equations (ODEs) to multi-relational graph convolutional networks. The proposed model preserves the continuous nature of dynamic multi-relational graph data and encodes both temporal and structural information into continuous-time dynamic embeddings. In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i.e., edge formation and dissolution. We perform extensive experiments on five benchmark datasets for temporal KG reasoning, showing our model{'}s superior performance on the future link forecasting task.",
}
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<abstract>There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary differential equations (ODEs) to multi-relational graph convolutional networks. The proposed model preserves the continuous nature of dynamic multi-relational graph data and encodes both temporal and structural information into continuous-time dynamic embeddings. In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i.e., edge formation and dissolution. We perform extensive experiments on five benchmark datasets for temporal KG reasoning, showing our model’s superior performance on the future link forecasting task.</abstract>
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%0 Conference Proceedings
%T Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs
%A Han, Zhen
%A Ding, Zifeng
%A Ma, Yunpu
%A Gu, Yujia
%A Tresp, Volker
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F han-etal-2021-learning-neural
%X There has been an increasing interest in inferring future links on temporal knowledge graphs (KG). While links on temporal KGs vary continuously over time, the existing approaches model the temporal KGs in discrete state spaces. To this end, we propose a novel continuum model by extending the idea of neural ordinary differential equations (ODEs) to multi-relational graph convolutional networks. The proposed model preserves the continuous nature of dynamic multi-relational graph data and encodes both temporal and structural information into continuous-time dynamic embeddings. In addition, a novel graph transition layer is applied to capture the transitions on the dynamic graph, i.e., edge formation and dissolution. We perform extensive experiments on five benchmark datasets for temporal KG reasoning, showing our model’s superior performance on the future link forecasting task.
%R 10.18653/v1/2021.emnlp-main.658
%U https://aclanthology.org/2021.emnlp-main.658
%U https://doi.org/10.18653/v1/2021.emnlp-main.658
%P 8352-8364
Markdown (Informal)
[Learning Neural Ordinary Equations for Forecasting Future Links on Temporal Knowledge Graphs](https://aclanthology.org/2021.emnlp-main.658) (Han et al., EMNLP 2021)
ACL