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Variational Attention for Commonsense Knowledge Aware Conversation Generation Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Knowledge Selective Adversarial Network for Link Prediction in Knowledge Graph

Authors : Kairong Hu, Hai Liu, Tianyong Hao

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

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Abstract

Knowledge Graphs (KGs) contain rich semantic information and are of importance to many downstream tasks. In order to enhance practical utilization of KGs, KG completion task, which is also called link prediction, is a newly emerging hot research topic. During KG embedding model training, negative sampling is a fundamental method for obtaining negative samples. Inspired by an adversarial learning framework KBGAN, this paper proposes a new knowledge selective adversarial network, named as KSGAN, using a knowledge selector for high-quality negative sampling to benefit link prediction. The performances of our model KSGAN are evaluated on three standard knowledge completion datasets: FB15k-237, WN18 and WN18RR. The results show that KSGAN outperforms a list of baseline models on all the datasets, demonstrating the effectiveness of the proposed model.

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Footnotes
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Metadata
Title
A Knowledge Selective Adversarial Network for Link Prediction in Knowledge Graph
Authors
Kairong Hu
Hai Liu
Tianyong Hao
Copyright Year
2019
Book
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-030-32232-8

Electronic ISBN: 978-3-030-32233-5

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-32233-5_14

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