@inproceedings{li-etal-2022-hismatch,
title = "{H}i{SM}atch: Historical Structure Matching based Temporal Knowledge Graph Reasoning",
author = "Li, Zixuan and
Hou, Zhongni and
Guan, Saiping and
Jin, Xiaolong and
Peng, Weihua and
Bai, Long and
Lyu, Yajuan and
Li, Wei and
Guo, Jiafeng and
Cheng, Xueqi",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.542",
doi = "10.18653/v1/2022.findings-emnlp.542",
pages = "7328--7338",
abstract = "A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (\textit{subject}, \textit{relation}, \textit{object}, \textit{timestamp}) to describe dynamic facts. TKG reasoning has facilitated many real-world applications via answering such queries as (\textit{query entity}, \textit{query relation}, \textit{?}, \textit{future timestamp}) about future. This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps. In addition, recent KGs provide background knowledge of all the entities, which is also helpful for the matching. Thus, in this paper, we propose the \textbf{Hi}storical \textbf{S}tructure \textbf{Match}ing (\textbf{HiSMatch}) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Besides, it adopts another encoder to integrate the background knowledge into the model. TKG reasoning experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6{\%} performance improvement in MRR, compared to the state-of-the-art baselines.",
}
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<abstract>A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (subject, relation, object, timestamp) to describe dynamic facts. TKG reasoning has facilitated many real-world applications via answering such queries as (query entity, query relation, ?, future timestamp) about future. This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps. In addition, recent KGs provide background knowledge of all the entities, which is also helpful for the matching. Thus, in this paper, we propose the Historical Structure Matching (HiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Besides, it adopts another encoder to integrate the background knowledge into the model. TKG reasoning experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines.</abstract>
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%0 Conference Proceedings
%T HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning
%A Li, Zixuan
%A Hou, Zhongni
%A Guan, Saiping
%A Jin, Xiaolong
%A Peng, Weihua
%A Bai, Long
%A Lyu, Yajuan
%A Li, Wei
%A Guo, Jiafeng
%A Cheng, Xueqi
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-hismatch
%X A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (subject, relation, object, timestamp) to describe dynamic facts. TKG reasoning has facilitated many real-world applications via answering such queries as (query entity, query relation, ?, future timestamp) about future. This is actually a matching task between a query and candidate entities based on their historical structures, which reflect behavioral trends of the entities at different timestamps. In addition, recent KGs provide background knowledge of all the entities, which is also helpful for the matching. Thus, in this paper, we propose the Historical Structure Matching (HiSMatch) model. It applies two structure encoders to capture the semantic information contained in the historical structures of the query and candidate entities. Besides, it adopts another encoder to integrate the background knowledge into the model. TKG reasoning experiments on six benchmark datasets demonstrate the significant improvement of the proposed HiSMatch model, with up to 5.6% performance improvement in MRR, compared to the state-of-the-art baselines.
%R 10.18653/v1/2022.findings-emnlp.542
%U https://aclanthology.org/2022.findings-emnlp.542
%U https://doi.org/10.18653/v1/2022.findings-emnlp.542
%P 7328-7338
Markdown (Informal)
[HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning](https://aclanthology.org/2022.findings-emnlp.542) (Li et al., Findings 2022)
ACL
- Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, and Xueqi Cheng. 2022. HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 7328–7338, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.