Gold: A Global and Local-aware Denoising Framework for Commonsense
  Knowledge Graph Noise Detection
Zheye Deng Weiqi Wang Zhaowei Wang Xin Liu Yangqiu Song
Commonsense Knowledge Graphs (CSKGs) are crucial for commonsense reasoning,
yet constructing them through human annotations can be costly. As a result,
various automatic methods have been proposed to construct CSKG with larger
semantic coverage. However, these unsupervised approaches introduce spurious
noise that can lower the quality of the resulting CSKG, which cannot be tackled
easily by existing denoising algorithms due to the unique characteristics of
nodes and structures in CSKGs. To address this issue, we propose Gold (Global
and Local-aware Denoising), a denoising framework for CSKGs that incorporates
entity semantic information, global rules, and local structural information
from the CSKG. Experiment results demonstrate that Gold outperforms all
baseline methods in noise detection tasks on synthetic noisy CSKG benchmarks.
Furthermore, we show that denoising a real-world CSKG is effective and even
benefits the downstream zero-shot commonsense question-answering task.