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FTMF: Few-shot temporal knowledge graph completion based on meta-optimization and fault-tolerant mechanism

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Abstract

Traditional knowledge graph completion mainly focuses on static knowledge graph. Although there are efforts studying temporal knowledge graph completion, they assume that each relation has enough entities to train, ignoring the influence of long tail relations. Moreover, many relations only have a few samples. In that case, how to handle few-shot temporal knowledge graph completion still merits further attention. This paper aims to propose a framework for completing few-shot temporal knowledge graph. We use self-attention mechanism to encode entities, use cyclic recursive aggregation network to aggregate reference sets, use fault-tolerant mechanism to deal with error information, and use similarity network to calculate similarity scores. Experimental results show that our proposed model outperforms the baseline models and has better stability.

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Funding

The work was supported by the National Natural Science Foundation of China (61402087), the Natural Science Foundation of Hebei Province (F2022501015), the Key Project of Scientific Research Funds in Colleges and Universities of Hebei Education Department (ZD2020402), and in part by the Program for 333 Talents in Hebei Province (A202001066).

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Authors and Affiliations

  1. School of Computer and Communication Engineering, Northeastern University (Qinhuangdao), Qinhuangdao, 066004, China

    Luyi Bai, Mingcheng Zhang, Han Zhang & Heng Zhang

  2. School of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Luyi Bai

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  1. Luyi Bai
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  2. Mingcheng Zhang
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  4. Heng Zhang
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Contributions

Luyi Bai: Conceptualization, Methodology, Formal analysis, Funding acquisition, Writing—original draft, Writing—review & editing; Mingcheng Zhang: Investigation, Validation, Formal analysis, Writing—original draft; Han Zhang: Validation, Formal analysis, Writing—original draft; Heng Zhang: Writing—review & editing.

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Correspondence to Luyi Bai.

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Bai, L., Zhang, M., Zhang, H. et al. FTMF: Few-shot temporal knowledge graph completion based on meta-optimization and fault-tolerant mechanism. World Wide Web 26, 1243–1270 (2023). https://doi.org/10.1007/s11280-022-01091-6

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