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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Boosting Knowledge Base Automatically via Few-Shot Relation Classification.

Ning Pang1, Zhen Tan1, Hao Xu1

  • 1Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, China.

Frontiers in Neurorobotics
|November 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for few-shot relation classification using distant supervision. It effectively reduces noise from automatically generated data, improving knowledge base construction.

Keywords:
distant supervisionfew-shot learningknowledge basemultiple instance learningrelation classification

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Relation Classification (RC) is crucial for building knowledge bases by extracting entity-relation triplets from text.
  • Traditional RC models struggle with new relations not seen during training.
  • Few-shot learning (FSL) addresses new relations but requires substantial labeled data, while distant supervision (DS) offers automatic data generation but introduces noise.

Purpose of the Study:

  • To investigate few-shot relation classification under distant supervision.
  • To develop a method that leverages DS for training RC models with limited labeled data.
  • To mitigate the impact of noisy labels inherent in DS.

Main Methods:

  • Proposing a novel approach for few-shot relation classification under distant supervision.
  • Incorporating multiple instance learning (MIL) methods into prototypical networks.
  • Utilizing MIL for sentence-level noise reduction in DS-generated data.

Main Results:

  • The proposed model demonstrates improved performance in the N-way K-shot setting.
  • The integration of MIL effectively reduces noise from distant supervision.
  • The method facilitates the training of relation classification models with less human labor.

Conclusions:

  • Few-shot relation classification under distant supervision is a viable approach for knowledge base construction.
  • Multiple instance learning is effective in handling noisy labels from distant supervision.
  • The proposed method offers a more efficient way to train relation classification models.