  Structured knowledge is important for many AI applications. Commonsense
knowledge, which is crucial for robust human-centric AI, is covered by a small
number of structured knowledge projects. However, they lack knowledge about
human traits and behaviors conditioned on socio-cultural contexts, which is
crucial for situative AI. This paper presents CANDLE, an end-to-end methodology
for extracting high-quality cultural commonsense knowledge (CCSK) at scale.
CANDLE extracts CCSK assertions from a huge web corpus and organizes them into
coherent clusters, for 3 domains of subjects (geography, religion, occupation)
and several cultural facets (food, drinks, clothing, traditions, rituals,
behaviors). CANDLE includes judicious techniques for classification-based
filtering and scoring of interestingness. Experimental evaluations show the
superiority of the CANDLE CCSK collection over prior works, and an extrinsic
use case demonstrates the benefits of CCSK for the GPT-3 language model. Code
and data can be accessed at https://candle.mpi-inf.mpg.de/.
