Commonsense knowledge about object properties, human behavior and general concepts is crucial for robust AI applications. However, automatic acquisition of this knowledge is challenging because of sparseness and bias in online sources. This paper presents Quasimodo, a methodology and tool suite for distilling commonsense properties from non-standard web sources. We devise novel ways of tapping into search-engine query logs and QA forums, and combining the resulting candidate assertions with statistical cues from encyclopedias, books and image tags in a corroboration step. Unlike prior work on commonsense knowledge bases, Quasimodo focuses on salient properties that are typically associated with certain objects or concepts. Extensive evaluations, including extrinsic use-case studies, show that Quasimodo provides better coverage than state-of-the-art baselines with comparable quality.

该研究提出了 Quasimodo 方法，通过从非标准网络来源提取出的候选断言，结合百科全书、图书和图像标签等的统计提示，在进行验证步骤时，重点考虑与某些对象或概念通常相关联的显著属性。与先前的常识知识库研究不同，Quasimodo 着重于获得常用的物体或概念属性，评估表明该方法比现有方法提供更好的覆盖率和可比质量。

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