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Few-shot Knowledge Reasoning Method based on Attention Mechanism

Published: 25 March 2020 Publication History

Abstract

As a core issue of knowledge graph research, knowledge graph reasoning and complementation technology have always been a hot topic of current research. Existing knowledge graph reasoning techniques usually requires a large amount of training for each relationship, and training each relationship requires a large number of training samples. Inspired by meta-learning [1], this paper combines the idea of meta-learning [1] with the attention mechanism [5] to be applied to knowledge reasoning. On the one hand, it greatly reduces the number of samples required for each relationship training, and also reduces the scale of the problem, and the accuracy is higher than the model without the attention mechanism; On the other hand, the extensibility of knowledge has been greatly improved. After the model training is expected to be completed, when dealing with the newly added relationship, there is no need to retrain the model.

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Cited By

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  • (2022)What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured DataACM Transactions on Intelligent Systems and Technology10.1145/351003013:3(1-45)Online publication date: 3-Mar-2022

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cover image ACM Other conferences
ICCPR '19: Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition
October 2019
522 pages
ISBN:9781450376570
DOI:10.1145/3373509
© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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  • Hebei University of Technology
  • Beijing University of Technology

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 March 2020

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Author Tags

  1. few-shot learning
  2. knowledge graph
  3. knowledge reasoning
  4. meta-learning

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • National Key Research and Development Program of China
  • National Natural Science Foundation of China

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ICCPR '19

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Cited By

View all
  • (2022)What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured DataACM Transactions on Intelligent Systems and Technology10.1145/351003013:3(1-45)Online publication date: 3-Mar-2022

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