research-article

Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reasoning

Authors:

Xinwang LiuAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1559 - 1568

https://doi.org/10.1145/3539618.3591711

Published: 18 July 2023 Publication History

Abstract

Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the timeline, has been widely studied to alleviate incompleteness issues in TKG, which is composed of a series of KG snapshots at different timestamps. Two types of information, i.e., intra-snapshot structural information and inter-snapshot temporal interactions, mainly contribute to the learned representations for reasoning in previous models. However, these models fail to leverage (1) semantic correlations between relationships for the former information and (2) the periodic temporal patterns along the timeline for the latter one. Thus, such insufficient mining manners hinder expressive ability, leading to sub-optimal performances. To address these limitations, we propose a novel reasoning model, termed RPC, which sufficiently mines the information underlying the Relational correlations and Periodic patterns via two novel Correspondence units, i.e., relational correspondence unit (RCU) and periodic correspondence unit (PCU). Concretely, relational graph convolutional network (RGCN) and RCU are used to encode the intra-snapshot graph structural information for entities and relations, respectively. Besides, the gated recurrent units (GRU) and PCU are designed for sequential and periodic inter-snapshot temporal interactions, separately. Moreover, the model-agnostic time vectors are generated by time2vector encoders to guide the time-dependent decoder for fact scoring. Extensive experiments on six benchmark datasets show that RPC outperforms the state-of-the-art TKGR models, and also demonstrate the effectiveness of two novel strategies in our model.

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Cited By

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Index Terms

Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph Reasoning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Temporal reasoning
  2. Machine learning
    1. Machine learning approaches
      1. Logical and relational learning

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cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

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Published: 18 July 2023

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SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Cited By

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https://doi.org/10.1109/TNNLS.2024.3378194
Liao LZheng LShang JLi XChen F(2025)ATPF: An Adaptive Temporal Perturbation Framework for Adversarial Attacks on Temporal Knowledge GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.351068937:3(1091-1104)Online publication date: Mar-2025
https://doi.org/10.1109/TKDE.2024.3510689
Liu YQiu RTang YYin HHuang Z(2025)PUMA: Efficient Continual Graph Learning for Node Classification With Graph CondensationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348569137:1(449-461)Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1109/TKDE.2024.3485691
Chen TYang LWang ZLong J(2025)A rule- and query-guided reinforcement learning for extrapolation reasoning in temporal knowledge graphsNeural Networks10.1016/j.neunet.2025.107186185(107186)Online publication date: May-2025
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Zhu YMa TSun SRong HBian YHuang K(2025)RTA: A reinforcement learning-based temporal knowledge graph question answering modelNeurocomputing10.1016/j.neucom.2024.128994617(128994)Online publication date: Feb-2025
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Cai WLi MShi XFan YZhu QJin H(2025)RE-SEGNN: recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecastingScience China Information Sciences10.1007/s11432-023-4073-y68:2Online publication date: 8-Jan-2025
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Yang FZhang YZhao XPang S(2024)Unleashing the Power of Decoders: Temporal Knowledge Graph Extrapolation with Householder TransformationSymmetry10.3390/sym1609116616:9(1166)Online publication date: 6-Sep-2024
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Chen KWang YSong XChen SYu HLi ALarson K(2024)Temporal knowledge graph extrapolation via causal subhistory identificationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/365(3298-3306)Online publication date: 3-Aug-2024
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