  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.
