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.
@article{arxiv.1909.01515,
title = {Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs},
author = {Mingyang Chen and Wen Zhang and Wei Zhang and Qiang Chen and Huajun Chen},
journal= {arXiv preprint arXiv:1909.01515},
year = {2019}
}