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

IJCNLP 2019 Mingyang ChenWen ZhangWei ZhangQiang ChenHuajun Chen

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... (read more)

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