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.

翻译：常识知识图谱（CSKGs）对于常识推理至关重要，但通过人工标注构建此类图谱成本高昂。因此，研究者提出了多种自动化方法以构建语义覆盖范围更广的CSKG。然而，这些无监督方法引入的虚假噪声会降低所生成CSKG的质量，且由于CSKG中节点与结构的独有特性，现有去噪算法难以有效应对此类噪声。为解决该问题，我们提出Gold（全局与局部感知去噪）——一种融合CSKG实体语义信息、全局规则及局部结构信息的去噪框架。实验结果表明，在合成噪声CSKG基准测试中，Gold在噪声检测任务上优于所有基线方法。此外，我们证实对真实CSKG进行去噪具有有效性，甚至能提升下游零样本常识问答任务的性能。