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Neighboring relation enhanced inductive knowledge graph link prediction via meta-learning

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Abstract

Inductive link prediction over knowledge graphs(KGs) aims to perform inference over a new graph with unseen entities. In contrast to transductive link prediction, which learns a fixed set of known entities and cannot handle unseen entities, inductive link prediction is closer to real-world scenarios because of the evolving nature of KGs. The recently proposed methods utilize the enclosing subgraph of candidate triples to obtain inductive ability. However, these methods explicitly encode the local subgraph surrounding each node pair and neglect the entity-independent neighboring relation modeling, resulting in poor generalization to unseen entities and high time complexity. Inspired by the success of meta-learning on out-of-domain generalization, we propose a novel NeighbOring relatiOn enhanceD meta-LearnEr(NOODLE) for inductive link prediction. NOODLE sufficiently exploits the neighboring relations from two aspects: commonsense semantic features and topological correlation information. Moreover, NOODLE rapidly learns transferable relations-specific information via meta-learning and generalizes well to unseen entities. Extensive experiments show that NOODLE outperforms the state-of-the-art methods on commonly used benchmark datasets in terms of effectiveness, generalization, and efficiency.

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Data Availability

All datasets used for experiments of this article are available from the public data repository at the website of https://github.com/kkteru/grail/tree/master/data

Notes

  1. Source code is available at https://github.com/LB0828/NOODLE

  2. https://github.com/MIRALab-USTC/KG-TACT

  3. https://github.com/shuwen-liu-ox/INDIGO

  4. https://github.com/Tebmer/SNRI

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Acknowledgements

This work is supported by the National Key Research and Development Program of China(2021ZD0113304), the General Program of Natural Science Foundation of China (NSFC)(62072346), and the Key Research and Development Project of Hubei Province (2021BBA099, 2021BBA029).

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  1. School of Computer Science, Wuhan University, Wuhan, 430060, Hubei Province, China

    Ben Liu, Miao Peng, Wenjie Xu & Min Peng

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Contributions

Ben Liu proposed the conceptualization and methodology, conducted the experiment, and wrote the original draft. Miao Peng performed data curation, prepared the figures, and performed review and editing. Wenjie Xu performed related work investigation, and performed review and editing. Min Peng proposed the research problem, supervised the whole research work, and performed review and editing.

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Correspondence to Min Peng.

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This article belongs to the Topical Collection: Special Issue on Knowledge-Graph-Enabled Methods and Applications for the Future Web

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Liu, B., Peng, M., Xu, W. et al. Neighboring relation enhanced inductive knowledge graph link prediction via meta-learning. World Wide Web 26, 2909–2930 (2023). https://doi.org/10.1007/s11280-023-01168-w

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