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/.

翻译：结构化知识对于许多人工智能应用至关重要。常识知识对于构建稳健的以人为中心的人工智能至关重要，但目前只有少数结构化知识项目涉足。然而，这些项目缺乏关于受社会文化背景影响的人类特质和行为的知识，而这些知识对于情境化人工智能至关重要。本文提出CANDLE，一种端到端的方法，用于大规模提取高质量的文化常识知识（CCSK）。CANDLE从庞大的网络语料库中提取CCSK断言，并将其组织成连贯的集群，涵盖3个主题领域（地理、宗教、职业）和多个文化方面（食物、饮品、衣着、传统、仪式、行为）。CANDLE包含基于分类的过滤和趣味性评分的审慎技术。实验评估表明，CANDLE的CCSK集合优于先前的工作，而一个外在用例则展示了CCSK对GPT-3语言模型的益处。代码和数据可访问https://candle.mpi-inf.mpg.de/。