Abstract
Conventional knowledge graph completion methods are effective for completing knowledge graphs (KGs), but they face significant challenges when dealing with relations with only a limited number of associative triples. To address the issue of incompleteness and long-tail distribution of relations in KGs, few-shot knowledge graph completion emerges as a promising solution. This approach predicts new triplets about a relation by leveraging only a handful of associated triples. Previous methods have focused on aggregating neighbor information and imposing sequential dependency assumptions. However, these methods can be counterproductive when they involve unrelated neighbors and rely on unrealistic assumptions, which hinders the learning of meta-representations. This paper proposes a simple and effective meta relational learning model (SMetaR) for few-shot knowledge graph completion that maintains the complete feature information of few-shot relations through a linear model. This approach effectively learns the meta-representation of few-shot relations and enhances meta-relational learning capabilities. Extensive experiments on two public datasets reveal that the model outperforms existing few-shot knowledge graph completion methods, demonstrating its effectiveness.
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Data Availability
Datasets are available on the website https://github.com/AnselCmy/MetaR. No datasets were generated or analysed during the current study.
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Acknowledgements
In the course of the research for this paper, we have received help and support from the following organizations, to whom we would like to express our heartfelt thanks: the AI General Computing Platform program of Hefei University.
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Conceptualization, S.C. and B.Y.; Methodology, S.C.; Validation, S.C. and B.Y.; Investigation, B.Y.; Writing-original draft preparation, S.C. and B.Y.; Writing-review and editing, B.Y.; Visualization, S.C. and C.Z.; supervision, B.Y. All authors have read and agreed to the published version of the manuscript.
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Chen, S., Yang, B. & Zhao, C. Simple and effective meta relational learning for few-shot knowledge graph completion. Optim Eng (2024). https://doi.org/10.1007/s11081-024-09880-w
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DOI: https://doi.org/10.1007/s11081-024-09880-w