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arXiv · 1909.01515

Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs

Mingyang Chen·Wen Zhang·Wei Zhang·Qiang Chen·Huajun Chen
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Abstract

Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples. We solve few-shot link prediction by focusing on transferring relation-specific meta information to make model learn the most important knowledge and learn faster, corresponding to relation meta and gradient meta respectively in MetaR. Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.

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Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen, Huajun Chen. 2019-09-04. Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs. https://arxiv.org/abs/1909.01515

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