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Relational multi-scale metric learning for few-shot knowledge graph completion

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Abstract

Few-shot knowledge graph completion (FKGC) refers to the task of inferring missing facts in a knowledge graph by utilizing a limited number of reference entities. Most FKGC methods assume a single similarity metric, which leads to a single feature space and makes it difficult to separate positive and negative samples effectively. Therefore, we propose a multi-scale relational metric network (MSRMN) specifically designed for FKGC, which integrates multiple scales of measurement methods to learn a more comprehensive and compact feature space. In this study, we design a complete neighbor random sampling algorithm to sample complete one-hop neighbor information, and aggregate both one-hop and multi-hop neighbor information to enhance entity representations. Then, MSRMN adaptively obtains prototype representations of relations and integrates three different scales of measurement methods to learn a more comprehensive feature space and a more discriminative feature mapping, enabling positive query entity pairs to obtain higher measurement scores. Evaluation of MSRMN on two public datasets for link prediction demonstrates that MSRMN attains top-performing outcomes across various few-shot sizes on the NELL dataset.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China Joint Fund Project [No.U23A20316], the Science and Technology Innovation 2030-“New Generation of Artificial Intelligence” Major Project [No.2021ZD0111000], and Henan Provincial Science and Technology Research Project [No.232102211033].

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Authors and Affiliations

  1. Zhengzhou University, Zhengzhou, China

    Yu Song, Mingyu Gui, Kunli Zhang, Zexi Xu, Dongming Dai & Dezhi Kong

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  1. Yu Song
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  2. Mingyu Gui
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  3. Kunli Zhang
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  4. Zexi Xu
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  5. Dongming Dai
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  6. Dezhi Kong
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Contributions

YS and MG led the method application, experiment conduction and the result analysis. DD and DK participated in the data extraction and preprocessing. ZX participated in the manuscript revision. KZ provided theoretical guidance and the revision of this paper.

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Correspondence to Kunli Zhang.

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Song, Y., Gui, M., Zhang, K. et al. Relational multi-scale metric learning for few-shot knowledge graph completion. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02083-w

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