计算机科学
元学习(计算机科学)
关系(数据库)
弹丸
人工智能
机器学习
过程(计算)
链接(几何体)
任务(项目管理)
数据挖掘
计算机网络
操作系统
经济
有机化学
化学
管理
作者
Shuangjia Zheng,Sijie Mai,Sun Ya,Haifeng Hu,Yuedong Yang
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-1
被引量:2
标识
DOI:10.1109/tkde.2022.3177212
摘要
Link prediction for knowledge graphs aims to predict missing connections between entities. Prevailing methods are limited to a transductive setting and hard to process unseen entities. The recently proposed subgraph-based models provide alternatives to predict links from the subgraph structure surrounding a candidate triplet. However, these methods require abundant known facts of training triplets and perform poorly on relationships that only have a few triplets. In this paper, we propose Meta-iKG, a novel subgraph-based meta-learner for few-shot inductive relation reasoning. Meta-iKG utilizes local subgraphs to transfer subgraph-specific information and to rapidly learn transferable patterns via meta-gradients. In this way, we find the model can quickly adapt to few-shot relationships using only a handful of known facts with inductive settings. Moreover, we introduce a large-shot relation updating procedure to ensure that our model can generalize well to both few-shot and large-shot relations. We evaluate Meta-iKG on inductive benchmarks sampled from the NELL and Freebase, and the results show that Meta-iKG outperforms the currently state-of-the-art methods in both few-shot scenarios and standard inductive settings.
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