@inproceedings{chen-etal-2022-rotateqvs,
title = "{R}otate{QVS}: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion",
author = "Chen, Kai and
Wang, Ye and
Li, Yitong and
Li, Aiping",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.402",
doi = "10.18653/v1/2022.acl-long.402",
pages = "5843--5857",
abstract = "Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention. In TKG, relation patterns inherent with temporality are required to be studied for representation learning and reasoning across temporal facts. However, existing methods can hardly model temporal relation patterns, nor can capture the intrinsic connections between relations when evolving over time, lacking of interpretability. In this paper, we propose a novel temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space (RotateQVS) and relations as complex vectors in Hamilton{'}s quaternion space. We demonstrate our method can model key patterns of relations in TKG, such as symmetry, asymmetry, inverse, and can capture time-evolved relations by theory. And empirically, we show that our method can boost the performance of link prediction tasks over four temporal knowledge graph benchmarks.",
}
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<abstract>Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention. In TKG, relation patterns inherent with temporality are required to be studied for representation learning and reasoning across temporal facts. However, existing methods can hardly model temporal relation patterns, nor can capture the intrinsic connections between relations when evolving over time, lacking of interpretability. In this paper, we propose a novel temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space (RotateQVS) and relations as complex vectors in Hamilton’s quaternion space. We demonstrate our method can model key patterns of relations in TKG, such as symmetry, asymmetry, inverse, and can capture time-evolved relations by theory. And empirically, we show that our method can boost the performance of link prediction tasks over four temporal knowledge graph benchmarks.</abstract>
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%0 Conference Proceedings
%T RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion
%A Chen, Kai
%A Wang, Ye
%A Li, Yitong
%A Li, Aiping
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F chen-etal-2022-rotateqvs
%X Temporal factors are tied to the growth of facts in realistic applications, such as the progress of diseases and the development of political situation, therefore, research on Temporal Knowledge Graph (TKG) attracks much attention. In TKG, relation patterns inherent with temporality are required to be studied for representation learning and reasoning across temporal facts. However, existing methods can hardly model temporal relation patterns, nor can capture the intrinsic connections between relations when evolving over time, lacking of interpretability. In this paper, we propose a novel temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space (RotateQVS) and relations as complex vectors in Hamilton’s quaternion space. We demonstrate our method can model key patterns of relations in TKG, such as symmetry, asymmetry, inverse, and can capture time-evolved relations by theory. And empirically, we show that our method can boost the performance of link prediction tasks over four temporal knowledge graph benchmarks.
%R 10.18653/v1/2022.acl-long.402
%U https://aclanthology.org/2022.acl-long.402
%U https://doi.org/10.18653/v1/2022.acl-long.402
%P 5843-5857
Markdown (Informal)
[RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion](https://aclanthology.org/2022.acl-long.402) (Chen et al., ACL 2022)
ACL