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One-Shot Relational Learning for Knowledge Graphs
lib:e1789b1e36d5bd25 (v1.0.0)
Authors: Wenhan Xiong,Mo Yu,Shiyu Chang,Xiaoxiao Guo,William Yang Wang
Where published: EMNLP 2018 10
ArXiv: 1808.09040
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1808.09040v1
Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion usually require a large number of training instances for each relation. However, we observe that long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge extracted by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.
Where published: EMNLP 2018 10
ArXiv: 1808.09040
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1808.09040v1
Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion usually require a large number of training instances for each relation. However, we observe that long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge extracted by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.
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