Commonsense knowledge (CSK) supports a variety of AI applications, from visual understanding to chatbots. Prior works on acquiring CSK, such as ConceptNet, have compiled statements that associate concepts, like everyday objects or activities, with properties that hold for most or some instances of the concept. Each concept is treated in isolation from other concepts, and the only quantitative measure (or ranking) of properties is a confidence score that the statement is valid. This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements. Our model captures four different dimensions of CSK statements: plausibility, typicality, remarkability and salience, with scoring and ranking along each dimension. For example, hyenas drinking water is typical but not salient, whereas hyenas eating carcasses is salient. For reasoning and ranking, we develop a method with soft constraints, to couple the inference over concepts that are related in in a taxonomic hierarchy. The reasoning is cast into an integer linear programming (ILP), and we leverage the theory of reduction costs of a relaxed LP to compute informative rankings. This methodology is applied to several large CSK collections. Our evaluation shows that we can consolidate these inputs into much cleaner and more expressive knowledge. Results are available at https://dice.mpi-inf.mpg.de.

中文翻译：

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多方面常识知识的联合推理

常识知识 (CSK) 支持各种 AI 应用程序，从视觉理解到聊天机器人。先前关于获取 CSK 的工作，例如 ConceptNet，已经编译了将概念（如日常对象或活动）与适用于该概念的大多数或某些实例的属性相关联的语句。每个概念都与其他概念分开处理，并且属性的唯一量化度量（或排名）是该陈述有效的置信度分数。本文旨在通过引入 CSK 语句的多方面模型和用于对相互关联的语句集进行联合推理的方法来克服这些限制。我们的模型捕捉 CSK 陈述的四个不同维度：合理性、典型性、显着性和显着性，并在每个维度上进行评分和排名。例如，鬣狗喝水是典型的但并不突出，而鬣狗吃尸体是突出的。对于推理和排名，我们开发了一种具有软约束的方法，以耦合对分类层次结构中相关概念的推理。推理被转换为整数线性规划 (ILP)，我​​们利用松弛 LP 的降低成本理论来计算信息排名。这种方法适用于几个大型 CSK 集合。我们的评估表明，我们可以将这些输入整合为更清晰、更具表现力的知识。结果可在 https://dice.mpi-inf.mpg.de 获得。推理被转换为整数线性规划 (ILP)，我​​们利用松弛 LP 的降低成本理论来计算信息排名。这种方法适用于几个大型 CSK 集合。我们的评估表明，我们可以将这些输入整合为更清晰、更具表现力的知识。结果可在 https://dice.mpi-inf.mpg.de 获得。推理被转换为整数线性规划 (ILP)，我​​们利用松弛 LP 的降低成本理论来计算信息排名。这种方法适用于几个大型 CSK 集合。我们的评估表明，我们可以将这些输入整合为更清晰、更具表现力的知识。结果可在 https://dice.mpi-inf.mpg.de 获得。