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

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.

[1]  Mo Yu,et al.  One-Shot Relational Learning for Knowledge Graphs , 2018, EMNLP.

[2]  Pasquale Minervini,et al.  Convolutional 2D Knowledge Graph Embeddings , 2017, AAAI.

[3]  Zhen Wang,et al.  Knowledge Graph Embedding by Translating on Hyperplanes , 2014, AAAI.

[4]  Richard S. Zemel,et al.  Prototypical Networks for Few-shot Learning , 2017, NIPS.

[5]  Sergey Levine,et al.  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.

[6]  Pieter Abbeel,et al.  A Simple Neural Attentive Meta-Learner , 2017, ICLR.

[7]  Oriol Vinyals,et al.  Matching Networks for One Shot Learning , 2016, NIPS.

[8]  Wei Zhang,et al.  Interaction Embeddings for Prediction and Explanation in Knowledge Graphs , 2019, WSDM.

[9]  Daan Wierstra,et al.  One-shot Learning with Memory-Augmented Neural Networks , 2016, ArXiv.

[10]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[11]  Xi Chen,et al.  Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks , 2019, NAACL.

[12]  Seungjin Choi,et al.  Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace , 2018, ICML.

[13]  Zhiyuan Liu,et al.  Representation Learning of Knowledge Graphs with Hierarchical Types , 2016, IJCAI.

[14]  Guillaume Bouchard,et al.  Complex Embeddings for Simple Link Prediction , 2016, ICML.

[15]  Huanbo Luan,et al.  Modeling Relation Paths for Representation Learning of Knowledge Bases , 2015, EMNLP.

[16]  Hong Yu,et al.  Meta Networks , 2017, ICML.

[17]  Nicholas Jing Yuan,et al.  Collaborative Knowledge Base Embedding for Recommender Systems , 2016, KDD.

[18]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[19]  Hans-Peter Kriegel,et al.  A Three-Way Model for Collective Learning on Multi-Relational Data , 2011, ICML.

[20]  Wei Zhang,et al.  Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning , 2019, WWW.

[21]  Markus Krötzsch,et al.  Wikidata , 2014, Commun. ACM.

[22]  Jason Weston,et al.  Open Question Answering with Weakly Supervised Embedding Models , 2014, ECML/PKDD.

[23]  Zhendong Mao,et al.  Knowledge Graph Embedding: A Survey of Approaches and Applications , 2017, IEEE Transactions on Knowledge and Data Engineering.

[24]  Jason Weston,et al.  Translating Embeddings for Modeling Multi-relational Data , 2013, NIPS.

[25]  Zhiyuan Liu,et al.  Learning Entity and Relation Embeddings for Knowledge Graph Completion , 2015, AAAI.

[26]  Estevam R. Hruschka,et al.  Toward an Architecture for Never-Ending Language Learning , 2010, AAAI.

[27]  Jianfeng Gao,et al.  Embedding Entities and Relations for Learning and Inference in Knowledge Bases , 2014, ICLR.

[28]  Gregory R. Koch,et al.  Siamese Neural Networks for One-Shot Image Recognition , 2015 .