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Commonsense knowledge about object properties, human behavior and generalconcepts is crucial for robust AI applications. However, automatic acquisitionof this knowledge is challenging because of sparseness and bias in onlinesources. This paper presents Quasimodo, a methodology and tool suite fordistilling commonsense properties from non-standard web sources. We devisenovel ways of tapping into search-engine query logs and QA forums, andcombining the resulting candidate assertions with statistical cues fromencyclopedias, books and image tags in a corroboration step. Unlike prior workon commonsense knowledge bases, Quasimodo focuses on salient properties thatare typically associated with certain objects or concepts. Extensiveevaluations, including extrinsic use-case studies, show that Quasimodo providesbetter coverage than state-of-the-art baselines with comparable quality.

Commonsense Properties from Query Logs and Question Answering Forums