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Temporal knowledge graph reasoning triggered by memories

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Abstract

The task of inferring missing facts using the temporal knowledge graph (TKG) is important and has been widely studied. Extrapolation in TKG inference is more challenging because no direct historical facts are available for predicting future events. Previous methods apply recursive embedding learning to solve the extrapolation problem. However, these methods primarily focus on recent historical facts and overlook the potential knowledge hidden in earlier facts. Moreover, the recursive embedding learning process can lead to imprecise knowledge propagation. To address these issues, we propose a memory-triggered decision-making (MTDM) network. It takes advantage of earlier historical facts to establish initial node representations and then updates node representations with recent historical facts. In particular, to enhance the prediction performance and efficiency, we update node representations of each timestamp in parallel using the proposed Res-GCN and then establish temporal correlations among these representations based on the designed control gating unit. To mitigate the issue of imprecise knowledge propagation, we leverage the facts most relevant to the missing knowledge to directly make predictions. This strategy improves the predictions by focusing on the most informative and contextual-free information. Additionally, we introduce the dissolution constraint, which enables us to analyse the process of event dissolution. Extensive experiments demonstrate that MTDM outperforms existing methods in terms of prediction accuracy and computational efficiency.

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Data Availability

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Code Availability

The code has been published on https://github.com/Dlut-lab-zmn/MTDM.

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Funding

This work is supported by the Ministry of Science and Technology of the People’s Republic of China (No. 2018AAA0102003), the Fundamental Research Funds for the Central Universities (No. 82232006, No. DUT22JC06), and the National Natural Science Foundation of China (No. 61902053).

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, 116081, Liaoning, China

    Mengnan Zhao & Baocai Yin

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China

    Lihe Zhang

  3. School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, 116081, Liaoning, China

    Yuqiu Kong

Authors
  1. Mengnan Zhao
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  2. Lihe Zhang
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  3. Yuqiu Kong
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  4. Baocai Yin
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Contributions

Conceptualization, M.Z. and L.Z.; methodology, M.Z. and L.Z. ; validation, M.Z. and Y.K.; formal analysis, M.Z. and Y.K.; investigation, M.Z.; resources, B.Y.; data curation, M.Z.; writing-original draft preparation, M.Z.; writing-review and editing, L.Z., Y.K., B.Y.; visualization, M.Z.; supervision, B.Y.; project administration, B.Y.; All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yuqiu Kong.

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Zhao, M., Zhang, L., Kong, Y. et al. Temporal knowledge graph reasoning triggered by memories. Appl Intell 53, 28418–28433 (2023). https://doi.org/10.1007/s10489-023-05015-3

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